Why?

Characterizing the functional architecture of the human visual cortex at the individual level is critical for understanding both normal visual perception and the consequences of disease. In this project, we will examine individual differences in this architecture, as well as uncertainty in the estimates, both along the cortical hierarchy and across cortical depth and in health and disease. We expect that this will provide important biomarkers for disease- as well as experience-related neuroplasticity. 

How?

We will apply innovative brain assessments tools to assess, in individuals, their population receptive field (pRF) and connective field (CF) profiles over cortical depth (i.e. perpendicular to the gray matter) and along the visual hierarchy (i.e. in different visual areas). This will be done in control participants but eventually also in participants with an ocular condition (e.g. glaucoma) or neurological damage (e.g. stroke, leading to hemianopia). We will use and refine modeling tools to assess uncertainty in the size of pRF and CF, and the direction of information flow. Moreover, we aim to gain better insight into the origins and stability of individual differences. Research visits (secondments) to network collaborators in Coimbra and Pisa are also foreseen.

What can you expect?

You will learn to use fMRI at 3 and 7 Tesla magnetic field strengths in combination with advanced computational approaches such as connective field analysis and population receptive field mapping. You will also learn to use and adapt these modeling techniques to quantify vision-related brain function at the individual level in both healthy individuals and patients, e.g. those with the eye disease glaucoma. Besides such project specific skills, you will also be able to gain experience in ophthalmic assessments such as perimetry and OCT. You will benefit from the broad and extensive academic exchange in our vibrant, interdisciplinary research teams as well as national and international scientific networks. 

Where?

You will be embedded in the Laboratory of Experimental Ophthalmology  (PIs: Cornelissen/Jansonius) which tightly collaborates with the Center for Neuroscience (CNC; PI: Renken) both at the University Medical Center Groningen (UMCG) of the University of Groningen, and (2)  the Spinoza Center for Cognitive Neuro-imaging in Amsterdam, all in the Netherlands (PI: Dumoulin). These sites focus on understanding the structure and function of the human visual cortex in health and eye and brain diseases, including the development of tools for use in basic and translational studies.

Who are we looking for?

You have a deep interest in translational and basic visual neuroscience and are interested in pursuing a career in this field. Experience with quantitative approaches in neuroscience, and well-developed programming skills (e.g. Python, R, Matlab) are close-to-essential requirements. Experience in systematic data-acquisition, brain-imaging, modeling, scientific writing and communication are all desirable skills. Candidates with a background in vision science, neuroscience, neuroimaging, engineering, experimental psychology, ophthalmology, biology, physiology, physics, mathematics or commensurate experiences will be considered.

References

Joana Carvalho, Remco J. Renken, and Frans W. Cornelissen (2019): Studying Cortical Plasticity in Ophthalmic and Neurological Disorders: From Stimulus-Driven to Cortical Circuitry Modeling Approaches Neural Plasticity: https://doi.org/10.1155/2019/2724101

Azzurra Invernizzi, Koen V. Haak, Joana C. Carvalho, Remco J. Renken, Frans W. Cornelissen (2022): Bayesian connective field modeling using a Markov Chain Monte Carlo approach, Neuroimage: https://doi.org/10.1016/j.neuroimage.2022.119688

Nicolás Gravel,  Remco J Renken,  Ben M Harvey,  Gustavo Deco,  Frans W Cornelissen, Matthieu Gilson (2020): Propagation of BOLD Activity Reveals Task-dependent Directed Interactions Across Human Visual Cortex; Cerebral Cortex, Volume 30, Issue 11, Pages 5899–5914, https://doi.org/10.1093/cercor/bhaa165

Why? 

Characterizing the functional architecture of the human visual cortex at the individual level is critical for understanding both normal visual perception and the functional consequences of disease. In this project, we will examine individual differences focusing on perceptual measures such as (cortical) contrast sensitivity (one’s ability to discern small differences in luminance), second-order contrast (e.g. orientation or motion contrast) and visual crowding (one’s ability to recognize objects in clutter). We expect these to provide new neural biomarkers for individual perceptual abilities in both health and disease.

How?

We will apply innovative brain assessment tools akin to population receptive field (pRF) modelling that aim to assess contrast sensitivity at the level of different cortical visual areas. This will be done in both controls and patients with an ocular condition (e.g. glaucoma) or neurological damage (e.g. hemianopia). In addition to applying existing stimuli and modeling tools, we will also create new ones to assess potentially sensitive new biomarkers, such as for the ability to discern differences in orientation contrast. Very interesting yet challenging will be assessing how (neural) visual crowding (or spatial integration), which can be modulated through feedback, is affected in various diseases already at an early stage, and how it is known to be sensitive to both long and short-term plasticity-dependent experience. Research visits (secondments) to network collaborators in Bergen and Coimbra are foreseen.

What can you expect? 

You will learn to use fMRI at 3 Tesla magnetic field strengths in combination with specialized wide-field visual stimulus display, eyetracking, and advanced computational modeling approaches such as pRF mapping. You will also learn to adapt these modeling techniques to quantify new biomarkers for vision-related brain function at the individual level in both healthy individuals and patients. Besides such project-specific skills, you will be able to gain experience in ophthalmic assessments such as perimetry and OCT. You will benefit from the broad and extensive academic exchange in our vibrant, interdisciplinary research teams as well as national and international scientific networks. 

Where? 

You will be embedded in the Laboratory of Experimental Ophthalmology  (PIs: Cornelissen/Jansonius) which collaborates tightly with the Center for Neuroscience (CNC; PI: Renken), both at the University Medical Center Groningen (UMCG) of the University of Groningen. These sites focus on understanding the structure and function of the human visual cortex in health and eye and brain diseases, including the development of tools for use in basic and translational studies.

Who are we looking for?

You have a deep interest in translational and basic visual neuroscience and are interested in pursuing a career in this field. Experience with quantitative approaches in neuroscience and well-developed programming skills (e.g. Python, R, Matlab) are close-to-essential requirements. Experience in systematic data-acquisition, brain-imaging, scientific writing and communication are all desirable skills. Candidates with a background in vision science, neuroscience, neuroimaging, engineering, experimental psychology, ophthalmology, biology, physiology, physics, mathematics or commensurate experiences will be considered.

References

Predictive masking of an artificial scotoma is associated with a system-wide reconfiguration of neural populations in the human visual cortex, NeuroImage, 245, 2021, 118690, https://doi.org/10.1016/j.neuroimage.2021.118690

Carlien Roelofzen, Marcus Daghlian,  Jelle A. van Dijk,  Maartje C. de Jong,  Serge O. Dumoulin: Modeling neural contrast sensitivity functions in human visual cortex; Imaging Neuroscience (2025) 3: imag_a_00469. https://doi.org/10.1162/imag_a_00469

Funda Yildirim, Joana Carvalho, Frans W. Cornelissen, A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping; NeuroImage, 2018, Pages 183-193, Link to archived version

Grillini, Alessandro & Renken, Remco & Cornelissen, Frans. (2019). Attentional Modulation of Visual Spatial Integration: Psychophysical Evidence Supported by Population Coding Modeling. Journal of Cognitive Neuroscience. 31. 1329-1342. Link to archived version

Why? 

Sensory processing depends on complex, recurrent loops of bottom-up and top-down information flow among interacting neurons. Decoding the directionality of this information exchange within cortical circuits is essential for understanding the neural computations that underpin brain dynamics, learning, sensory training, and neuroplasticity, particularly following brain injury.

This project aims to harness ultrahigh spatiotemporal resolution fMRI, combined with bidirectional models of connectivity, to dissociate bottom-up (feedforward) and top-down (feedback) signals. Furthermore, it seeks to map individual variability in cortical circuit organization within the human brain. 

How? 

We will combine advanced MRI scanning with computational models of interlayer communication, specifically, layer-specific connective field models, to characterize the temporal dynamics of information flow between cortical layers and interconnected regions in individual human brains. The project will be developed at the University of Coimbra and it includes two planned secondments: industry secondment at ICON (NL), focusing on linking individual differences in brain networks to outcomes relevant to drug trials, academic international secondment at Tilburg University (NL), dedicated to developing advanced models of bottom-up and top-down signaling.

What can you expect? 

The doctoral candidate (DC) will develop strong computational and analytical skills, becoming an expert in neuroimaging and visual neuroscience. Comprehensive training will be provided in areas such as personal development, grant writing, business plan writing,  leadership, education and research methods.

Where? 

The project will be based at the University of Coimbra, a historic and vibrant city that hosts one of Europe’s oldest universities, offering an excellent quality of life and an inspiring academic environment. The DC will join the Visual Neuroscience Lab, supervised by Dr. Joana Carvalho and co-supervised by Dr. Zohar Tal. The lab provides access to state-of-the-art neuroimaging and neurophysiological tools, including a 3T MRI scanner (Coimbra), 7T MRI (through

international collaborations), EEG, eye tracking, neuromodulation (TMS with neuronavigation and tDCS), and motion tracking systems.

Who are we looking for?

We are seeking a motivated and talented PhD candidate to join the Visual Neuroscience Lab at the Faculty of Psychology, UC. The successful candidate will contribute to advancing our understanding of individual variability in perception and cortical circuitry, using cutting-edge fMRI and computational modeling approaches (e.g., population receptive field and connective field models). The DC should hold aMSc in Biomedical Engineering, Psychology, Data Science, Neuroscience, or related fields. Previous experience with fMRI and/or eye tracking is highly desirable. Strong analytical and programming skills (Python or MATLAB). Fluency in English and excellent communication skills.

References

Carvalho, Joana, Fernandes, Francisca, Valente, Mafalda, Haak, Koen, Shemesh, Noam. 2025 . “Layer connective fields from ultrafast resting-state fMRI differentiate feedforward from feedback signaling.” (under revision) https://doi.org/10.1101/2025.02.23.639720

Haak, Koen V., Jonathan Winawer, Ben M. Harvey, Remco Renken, Serge O. Dumoulin, Brian A. Wandell, and Frans W. Cornelissen. 2013. “Connective Field Modeling.” NeuroImage 66 (February):376–84.

Carvalho, Joana, Azzurra Invernizzi, Khazar Ahmadi, Michael B. Hoffmann, Remco J. Renken, and Frans W. Cornelissen. 2020. “Micro-Probing Enables Fine-Grained Mapping of Neuronal Populations Using fMRI.” NeuroImage 209 (April):116423.

Why? 

The excitatory/inhibitory (E/I) dynamic interplay plays a fundamental role in shaping the complex landscape of neurological development. An imbalance in E/I mechanisms underlies many neurodevelopmental disorders. Mounting evidence suggests that Autism Spectrum Disorders (ASD) stem from an increased E/I ratio, leading to hyperexcitability of cortical circuits. Similarly, amblyopia, the leading cause of vision impairment in children, arises from E/I imbalance driven by unequal visual input between the two eyes. Understanding the neural computations through which E/I mechanisms modulate brain topography and cortical connectivity (feedback and feedforward) is essential for explaining how these imbalances give rise to visual, social, learning, and cognitive impairments. This knowledge has enormous diagnostic potential for developing early therapeutic interventions. In this project, we will develop non-linear models describing how E/I dynamics shape receptive field structures, visual maps, and cortico-cortical connections. Furthermore, we will investigate how disruptions in E/I balance, particularly in neurodevelopmental disorders, contribute to individual variability in brain topography, behaviour, and sensory perception.

How? 

 This project will adopt a multimodal approach, combining MRI and EEG with computational models of brain topography and connectivity. This will allow us to establish direct links between fMRI signals and E/I activity measured via EEG. Furthermore the DC will develop non-linear models of cortical circuits capable of characterizing, at an individual level, the interplay between E/I dynamics, brain topography, and connectivity. The project will be conducted at the University of Coimbra and will include two secondments: a cross-sectoral placement at the Champalimaud Foundation (clinical centre), focused on validating fMRI-derived E/I measures using simultaneous fMRI–fiber photometry in animal models; and an academic secondment at UMCG (NL), dedicated to developing non-linear Bayesian connective field (CF) and population receptive field (pRF) models in neurotypical and atypical individuals.

What can you expect? 

The doctoral candidate (DC) will develop strong computational and analytical skills, becoming an expert in neuroimaging ( MRI, EEG, Calcium Imaging) and visual neuroscience. Comprehensive training will be provided in areas such as personal development, grant writing, business plan writing,  leadership, education and research methods.

Where? 

The project will be based at the University of Coimbra, a historic and vibrant city that hosts one of Europe’s oldest universities, offering an excellent quality of life and an inspiring academic

environment. The DC will join the Visual Neuroscience and the Proaction Lab,  and will be supervised by Dr. Joana Carvalho and co-supervised by Prof. Jorge Almeida. The lab provides access to state-of-the-art neuroimaging and neurophysiological tools, including a 3T MRI scanner (Coimbra), 7T MRI (through international collaborations), EEG, eye tracking, neuromodulation (TMS with neuronavigation and tDCS), and motion tracking systems.

Who are we looking for?

We are seeking a motivated and talented PhD candidate to join the Visual Neuroscience and Proaction Labs at the Faculty of Psychology, University of Coimbra. The successful candidate will contribute to advancing our understanding of individual variability in perception, brain topography, and cortical circuitry using multimodal approaches that combine fMRI, EEG, calcium recordings, and computational modelling (e.g., population receptive field and connective field models). The ideal candidate will hold an MSc in Biomedical Engineering, Psychology, Data Science, Neuroscience, or a related field. Previous experience with fMRI and/or eye tracking is highly desirable, along with strong analytical and programming skills (Python or MATLAB). Fluency in English and excellent communication skills are required.

References

Carvalho, Joana, Azzurra Invernizzi, Khazar Ahmadi, Michael B. Hoffmann, Remco J. Renken, and Frans W. Cornelissen. 2020. “Micro-Probing Enables Fine-Grained Mapping of Neuronal Populations Using fMRI.” NeuroImage 209 (April):116423.

Invernizzi, Azzurra, Koen V. Haak, Joana C. Carvalho, Remco J. Renken, and Frans W. Cornelissen. 2022. “Bayesian Connective Field Modeling Using a Markov Chain Monte Carlo Approach.” NeuroImage 264 (December):119688.

Haak, Koen V., Jonathan Winawer, Ben M. Harvey, Remco Renken, Serge O. Dumoulin, Brian A. Wandell, and Frans W. Cornelissen. 2013. “Connective Field Modeling.” NeuroImage 66 (February):376–84.

Carvalho, Joana, Remco J. Renken, and Frans W. Cornelissen. 2021. “Predictive Masking of an Artificial Scotoma Is Associated with a System-Wide Reconfiguration of Neural Populations in the Human Visual Cortex.” NeuroImage. https://doi.org/10.1016/j.neuroimage.2021.118690.

Why? 

Each human brain is unique, with neural circuits adapting to diverse challenges and experiences throughout life. This adaptability, known as neuroplasticity, is crucial for development and learning, but it also influences how our brains respond to various diseases, including those affecting the brain itself. Consequently, each brain possesses unique circuits, making no two brains respond identically.

Traditionally, such differences were dismissed as noise. However, we now recognise this as potentially valuable information that must be quantified and interpreted to advance personalised medicine and rehabilitation, as well as our understanding of ourselves as unique individuals.

The complexity of the visual brain makes it challenging to interpret neuroimaging measurements at specific recording sites due to various factors, such as noise and ripple effects originating elsewhere in the brain. Theoretical computational models can help interpret these measurements, enabling a more precise link to the underlying variables of interest. This position aims to develop biologically realistic artificial neural networks and experiment with them to understand the effects of individual differences in network parameters and artificial lesions when probed using population receptive field (pRF) and connective field (CF) modelling approaches.

How? 

You will develop a simulation framework that supports the design and interpretation of more targeted experimental studies into individual differences in health and disease. This framework will involve biologically realistic artificial neural networks and use them to simulate the effects of individual differences and artificial lesions on pRF and CF measurements. By linking complex neuroimaging data to underlying variables of interest, you will interpret the results.

You’ll collaborate with other doctoral candidates in our network to advance these techniques and apply them to gain insights into individual differences in healthy controls and individuals with ocular and neurological damage. 

Most of the research will be conducted at the Tilburg University under the supervision of Koen Haak and Hinke Halbertsma. The project will also involve cross-sector and

cross-country secondments at Adastra in Germany and the University of Pisa in Italy, under supervision of Paola Binda.

What can you expect? 

The candidate will learn how to develop biologically realistic artificial neural networks and how to analyse behavioural and imaging data with cutting edge approaches.  Beyond the specific skills acquired during the project the candidate will attend training events to learn multiple skills that are transferable to employment in the academic and commercial/industrial sectors.

Where? 

Most of the candidate’s time will be spent at the Department of Intelligent Systems and Research Center for Cognitive Science and Artificial Intelligence of Tilburg University, where core facilities include research-dedicated high-performance GPU cluster computing optimised for training very large deep neural networks. 

Who are we looking for?

We are seeking an individual with excellent quantitative and programming skills and who received training in machine learning and deep neural networks. A keen interest (essential) and experience (desirable) in visual perception, computational neuroimaging and computational modelling using deep neural networks are also valued. Previous research project experience would be ideal.

References

Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., … & Eslami, S. A. (2018, July). Conditional neural processes. In International conference on machine learning (pp. 1704-1713). PMLR.

Wandell, B. A., & Winawer, J. (2015). Computational neuroimaging and population receptive fields. Trends in cognitive sciences, 19(6), 349-357.

Why? 

Each human brain is unique, with neural circuits adapting to diverse challenges and experiences throughout life. This adaptability, known as neuroplasticity, is crucial for development and learning, but it also influences how our brains respond to various diseases, including those affecting the brain itself. Consequently, each brain possesses unique circuits, making no two brains respond identically. 

Traditionally, such differences were dismissed as noise. However, we now recognise this as potentially valuable information that must be quantified and interpreted to advance personalised medicine and rehabilitation, as well as our understanding of ourselves as unique individuals.

Ideally, these features are studied through longitudinal research, but such studies are often costly, time-consuming, and logistically difficult. To address this challenge, this project aims to develop a biologically constrained generative modelling framework to interpret cross-sectional neuroimaging data in terms of individualised lifespan trajectories.

How? 

This project aims to develop a biologically constrained generative modelling framework, that interprets cross-sectional data in terms of individual lifespan trajectories. We will specifically focus on fMRI-based population receptive field and connective field estimates in human visual cortex. You will collaborate with other doctoral candidates in our network to advance these techniques and apply them to gain insights into individual differences in healthy controls and individuals with ocular and neurological damage. 

Most of the research will be conducted at the Tilburg University under the supervision of Koen Haak and Gorkem Saygili, while secondments will be at University of York under the supervision of Tony Morland and at Innovision, York, UK, under the supervision of Andre Gouws.

What can you expect? 

The candidate will learn how to perform deep generative modelling and how to analyze behavioural and imaging data with cutting edge approaches.  Beyond the specific skills acquired during the project the candidate will attend training events to learn multiple

skills that are transferable to employment in the academic and commercial/industrial sectors.

Where? 

Most of the candidate’s time will be spent at the Department of Intelligent Systems and Research Center for Cognitive Science and Artificial Intelligence of Tilburg University, where core facilities include research-dedicated high-performance GPU cluster computing optimised for training very large deep neural networks. At the York Neuroimaging Centre there are facilities that include a Siemens Prisma 3T scanner and associated computing facilities for the analysis of neuroimaging data. The University of York also provides access to relevant neuroimaging datasets as well as vision testing facilities in the Department of Psychology. 

Who are we looking for?

We are seeking an individual with excellent quantitative and programming skills and who received training in machine learning and deep neural networks. A keen interest (essential) and experience (desirable) in visual perception, computational neuroimaging and disease progression modeling using deep generative modelling frameworks are also valued. Previous research project experience would be ideal.

References

Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., … & Eslami, S. A. (2018, July). Conditional neural processes. In International conference on machine learning (pp. 1704-1713). PMLR.

Wandell, B. A., & Winawer, J. (2015). Computational neuroimaging and population receptive fields. Trends in cognitive sciences, 19(6), 349-357.

Why? 

Understanding how individual human brains process visual information over time is essential for advancing personalized neuroscience and clinical care. Most neuroimaging studies average data over groups, missing valuable individual variations. IndiBrain aims to overcome this by using advanced models to capture, at millisecond resolution, the unique feedforward and feedback processes that shape perception in each brain. 

How? 

You will develop and apply state-of-the-art population receptive field (pRF) models linking magnetoencephalography (MEG) and ultra-high field (7T) functional Magnetic Resonance Imaging (MRI), leveraging, respectively, their high temporal and spatial resolution. You will separate and quantify individual-specific feedforward and feedback processing in the early visual cortex using models sensitive to fast changes in neural activity. Primary work will be at the Netherlands Institute for Neuroscience (NIN, Serge Dumoulin and Maartje de Jong) and Amsterdam University Medical Center (Amsterdam UMC, Arjan Hillebrand), with cross-sector and cross-country secondments. You will work alongside a consortium of leading neuroscientists, data scientists, and industrial innovators.

What can you expect? 

Comprehensive training in cognitive neuroscience, computational modeling, MEG, 7T fMRI. You’ll experience academic research and industry applications, participate in international training events, and contribute directly to cutting-edge personalized neuroimaging research with significant input into clinical applications.

Where? 

Main base: Netherlands Institute for Neuroscience (Amsterdam, Netherlands). Access to world-class MEG/fMRI facilities, advanced computation, and collaborative international environment.

Who are we looking for?

A motivated, quantitative researcher with experience (or a strong interest) in neuroimaging, data-analyses, computational neuroscience, and programming skills (e.g., Python). Teamwork, initiative, and enthusiasm for method development combined with interest in cognitive neuroscience applications.

References

1. Eickhoff K, Hillebrand A, de Jong MC, Dumoulin SO (2024) Population receptive field models capture the event-related magnetoencephalography response with millisecond resolution. Imaging Neuroscience. 2024. doi:10.1162/imaga00285  
2. Dumoulin SO, Wandell BA (2008). Population receptive field estimates in human visual cortex. NeuroImage. doi:10.1016/j.neuroimage.2007.09.034

Why? 

There are a number of neurological conditions that affect eye movements and the perception of space. Eye tracking is a promising tool to aid in the diagnosis and prognosis of such disorders. Where treatments are available, the level of success of a treatment varies significantly between patients due to the variability in how the brain is affected and how it responds. Further insights are needed into the connection between brain function and eye movements in order to better predict response to treatment for the individual. 

How? 

The candidate will combine and optimize pre-existing hardware and software solutions to create a system for acquiring and analyzing functional MRI and eye movement data. This includes designing appropriate visual stimuli, followed by acquisition and processing of both data types including receptive field and connectivity modelling. In addition to co-supervision from NordicNeuroLab and the University of Bergen Faculty of Psychology, secondments within the Universities of Bergen and Groningen will aid in the development of the necessary skills and techniques.

What can you expect? 

The candidate will gain experience in both hardware and software engineering, MRI data acquisition, eye tracking and data analysis. This will be gained through practical hands-on training, supplemented by courses (both within the University of Bergen and externally), and secondments where needed. There will be an opportunity to attend international conferences as well as networking events to promote learning and personal development.

Where? 

The majority of time will be spent working between the NordicNeuroLab headquarters in Bergen, Norway and the Faculty of Psychology at the University of Bergen, with experimental work on the 3T MRI scanners at Hauckeland University Hospital, Bergen. NordicNeuroLab have a wealth of expertise in hardware engineering, in addition to application scientists with experience in applications of functional MRI and eye tracking. Researchers from the University of Bergen bring significant expertise in functional MRI data acquisition, processing and analysis, with a particular focus on neurological and psychiatric disorders.  

Who are we looking for?

We would like to appoint a motivated individual with an interest in the application of neuroscience and neuroimaging to neurological disorders. The candidate should be self-motivated, independent and creative, and willing to work within a diverse team. Good communication skills, both written and verbal, are essential, as is fluency in English.

References

Wang YL et al. (2025) Saccadic eye movements in neurological disease: cognitive mechanisms and clinical applications. Journal of Neurology (2025) 272:539. doi: https://doi.org/10.1007/s00415-025-13275-x.

Specht K (2020) Current Challenges in Translational and Clinical fMRI and Future Directions. Front. Psychiatry 10:924. doi: 10.3389/fpsyt.2019.00924.