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Application for admission in 2026-27 has closed. Applications for 2027-28 will open early September 2026

** Applications for standard admission and consideration in the University funding competitions are now closed for 2026-27 entry. However, please see the opportunity in the Jozwik lab below (application deadline: 13th March 2026 for October 2026 entry). In addition, if you have secured funding from a charity, your home country or via some other route (e.g. NIHR doctoral training award), we can still consider making an offer of admission for October 2026 entry; we are very happy to discuss such cases further, and so please do reach out to us. **

We welcome PhD or Research MPhil applications from UK or Overseas applicants

Our postgraduate community is diverse and international with up to 60 postgraduate students at any time. Our programmes are similarly diverse, spanning a wide variety of topics and approaches that include experimental cognitive psychology, neuropsychology, computational modelling, and neuroimaging using MRI, MEG, and EEG. We invite applications from those wishing to pursue research that falls within any of our research programmes – though before applying, we very highly recommend that you reach out to a potential supervisor to discuss your research ideas. 

Our standard admissions window opens 10th Sept 2025 for an Oct 2026 start, with the following key dates (though please crosscheck carefully against those specified in the applicant portal):

Postgraduate Open Days

The University hosts open days, where you can learn more about courses that interest you, what it is like to be a postgraduate, and funding. You can explore opportunities and book places here. 

Trying to decide what research to propose for a PhD or MPhil?

Please begin by identifying a potential supervisor and contacting them to learn about current opportunities in their group. You can read more about ongoing research on the webpages of our programme leaders and other researchers (including that of Dr. Kamila Jóźwik, here). Before applying, it is important to find out whether they are taking students currently and would be happy to supervise a project based on your interests. They may, for example, suggest an alternative project that is closer to their research interests or some articles that you should read for inspiration. Alternatively, they might  suggest that another supervisor might be more appropriate for your particular interests.

Once you have the support of a supervisor, whatever your idea or research question, you will need to produce a 1-page MPhil or 2-page PhD proposal to upload with your application. We recommend you complete this early as your prospective supervisor may be willing to provide helpful feedback on a single draft of your research proposal before you upload it with your application.

How to apply

Further information and details on our standard admission processes and how to apply are here, though please see some additional specific opportunities below.

1. *Fully-funded PhD in visual computational neuroscience – October 2026 start*

Supervisor: Dr. Kamila Maria Jozwik, Kamila Maria Jozwik lab, University of Cambridge 

Application deadline: 13th March 2026

Click here to apply: https://www.postgraduate.study.cam.ac.uk/courses/directory/cvbspdbsc

PhD fees status: Home fees only (https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status), 4 years, fully funded

The Jozwik lab studies visuo-semantic cognition combining cognitive science, neuroscience, and computational modelling. The lab’s research has focused on probing specific visual dimensions in the context of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate).  We also began exploring how to apply our expertise in visuo-semantic cognition and AI to neurotechnology and mental health applications.

The PhD student is welcome to work on one (or more) of the three aspects of the research programme funded by the Royal Society that aims to disentangle and model behaviourally-relevant visual and semantic dimensions (characteristics of objects: ”curved”, ”pink”, ”having eyes”, “being animate”, ”having agency”, or ones that are hard to name) of visual cognition in the human brain, while increasing the ecological validity of experiments (including mobile EEG and immersive technologies), in the light of the below three aims. Note Dr. Jozwik would be happy to discuss PhD projects related to these aims, as there is some flexibility in research directions.

1) characterise behaviourally-relevant visual and semantic dimensions by the use of large-scale brain imaging datasets of responses to images and model these representations with computational models and validate these predictions in follow-up neuroimaging experiments,

2) define and model dimensions related to the perception of animacy when interacting with objects and people using videos (behaviour, fMRI, and MEG),

3) determine to what extent these brain representations and dimensions change when humans are immersed in the environment (VR/AR and/or mobile EEG).

The ideal candidate will have extensive experience in programming in Python or Matlab and data analysis (essential, please note that coursework coding during an undergraduate or Master’s degree will likely not be enough), substantial research experience (essential, e.g., through research MPhil/Master’s degree, or research assistant job), experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis, experience in machine learning and AI, a collaborative approach to doing science and willingness to help other lab members, and curiosity and motivation to work on the proposed or related research questions.

Before applying, please contact Kamila Maria Jozwik (Royal Society University Research Fellow and Assistant Research Professor, jozwik.kamila@gmail.com or kj287@cam.ac.uk). In the initial email, please include: your CV; information about your programming, computational modelling, and relevant research, data collection and analysis experience (fMRI, M/EEG, neuromodulation, electrophysiology, behaviour); and details of journal and conference publications, preprints, and research theses. Please also ask 2-3 of your referees, ideally with whom you have worked on research projects, to email their reference letters to Dr. Jozwik.

Lab research environment: The Jozwik lab is based at the MRC Cognition and Brain Sciences Unit, University of Cambridge, with links to broader Cambridge (e.g., Cambridge NeuroWorks powered by Advanced Research and Invention Agency) and international scientific ecosystems (e.g., the Center for Brains, Minds & Machines, now MIT Quest for Intelligence). The Unit has an on-site 3T fMRI scanner (with access to a 7T fMRI scanner within cycling distance), an MEG scanner, EEG systems, Focused Ultrasound, Transcranial Magnetic Stimulation, and dedicated methods and computing support staff. The Unit runs two MPhil Programs: Cognitive Neuroscience and NeuroAI, and PhD students have the opportunity to supervise MPhil students. The lab values commitment to rigorous, open science, supports diversity in all its meanings, and drives curiosity in a supportive, multidisciplinary, and international research environment.

2. Doctoral Training Programme in Medical Research (DTP-MR) PhD Studentship – 3 month rotation projects

* The application window for 2026-27 entry has closed for these studentships. But for individuals who are awarded DTP-MR funding, CBU is offering the following 3-month rotation projects. *

The School of Clinical Medicine also provides a small number of fully-funded DTP-MR PhD Studentships. Applications are also made via the standard CBU admission processes. The studentship covers study for four years and also gives access to a range of researcher training and development opportunities designed for the DTP-MR student cohort. The programme is typically structured with the first 6 months undertaken as two 3-month rotation projects in different laboratories. Students who have a clear research plan can however request to omit the rotations and commence study in their preferred lab. DTP-MR studentships are potentially available for any research project within the School of Clinical Medicine although the programme is particularly interested in funding projects within the CBU areas of Data Science for Health and Neurosciences and Mental Health (as well as Infections and Immunity and Molecular and Cellular Mechanisms of Disease).

Possible rotation projects for completion at the CBU include the following. Please contact the Supervisor for more information.

Awakening Phantom Limbs with Brain Stimulation (Supervised by Tamar Makin, https://plasticity-lab.com/)

Many amputees continue to feel a missing limb. These phantom sensations provide a unique opportunity to study how the brain estimates the state of the body when sensory signals are incomplete. Our project tests the idea that phantom experience reflects an active process in which the brain combines internal motor predictions with limited peripheral evidence. Using transcranial magnetic stimulation (TMS) and high-density electromyography (HD-EMG), we experimentally manipulate and measure motor signals to understand how movement is inferred when the limb is no longer present. This work contributes to a broader effort to explain phantom limb pain in terms of fundamental sensorimotor computation.

During this 3-month rotation, the student will join an ongoing study with amputee and control participants. They will gain hands-on experience collecting TMS and EMG data, learning how to run human neurophysiology experiments and conduct structured interviews about phantom sensations. The student will also be introduced to EMG and behavioural data processing analysis. For those interested, there is an optional opportunity to explore computational models describing how the brain combines evidence and expectations when judging movement. The project offers practical training in brain stimulation, human physiology, and experimental neuroscience within a clinically relevant research setting.

Understanding the different behavioural-emotional profiles associated with self-reported social media addiction in adolescence (Supervised by Amy Orben, Digital Mental Health Group/)

There is widespread public concern that young people are addicted to social media, yet little is known about the heterogeneity of underlying mechanisms contributing to perceived addiction. Although research typically treats perceived social media addiction as a monolith (Xiao et al., 2025), characterising such heterogeneity is important, because different underlying behavioural-emotional profiles may respond differently to interventions (Conrod et al., 2008).

We have recently analysed the Millennium Cohort Study (MCS), a large, nationally-representative cohort dataset (n=10,952), and using a data-driven clustering analysis, uncovered three distinct behavioural-emotional profiles associated with self-reported social media addiction: a ‘social’ profile (representing 84% of those with self-reported addiction), a ‘risk behaviour’ profile (11%) and a ‘low wellbeing’ profile (5%) (Turner et al., in prep). However, the significance of these distinct profiles in terms of objective social media behaviour and longitudinal vulnerability to social media use is not known.

In this rotation project, the student will extend our MCS findings in the Adolescent Brain and Cognitive Development (ABCD) study, a birth cohort dataset with 11,875 children recruited from the United States (https://abcdstudy.org). First, the student would aim to replicate the clustering analysis to determine whether the variety and prevalence of behavioural-emotional profiles associated with self-reported social media addiction is robust across two large-scale samples (using responses to the Social Media Addiction Questionnaire at age 15). Second, the student will develop these findings in two potential directions. First, the student can analyse whether separate behavioural-emotional profiles show different longitudinal relationships between social media use and mental health, using longitudinal statistical models (c.f. (Orben et al., 2022)). Second, the student can analyse the objective real-world smartphone use data collected in the ABCD (Alexander et al., 2024) using a novel network analysis method also recently developed by our team (Turner et al., 2026), to understand how distinct profiles relate to objective digital technology use.

In summary, this project will characterise the distinct behavioural-emotional profiles underlying perceived social media addiction, which could inform and redirect research into the mechanisms of social media addiction, as well as policy and clinical interventions. The rotation student will have the opportunity to advance this research through advanced and novel computational techniques, for which our lab has expertise to provide support if necessary.

References: Alexander, J.D., Linkersdörfer, J., Toda-Thorne, K., Sullivan, R.M., Cummins, K.M., Tomko, R.L., Allen, N.B., Bagot, K.S., Baker, F.C., Fuemmeler, B.F., Hoffman, E.A., Kiss, O., Mason, M.J., Nguyen-Louie, T.T., Tapert, S.F., Smith, C.J., Squeglia, L.M., Wade, N.E., 2024. Passively sensing smartphone use in teens with rates of use by sex and across operating systems. Sci Rep 14, 17982. https://doi.org/10.1038/s41598-024-68467-8; Conrod, P.J., Castellanos, N., Mackie, C., 2008. Personality‐targeted interventions delay the growth of adolescent drinking and binge drinking. Child Psychology Psychiatry 49, 181–190. https://doi.org/10.1111/j.1469-7610.2007.01826.x; Orben, A., Przybylski, A.K., Blakemore, S.-J., Kievit, R.A., 2022. Windows of developmental sensitivity to social media. Nat Commun 13, 1649. https://doi.org/10.1038/s41467-022-29296-3; Xiao, Y., Meng, Y., Brown, T.T., Keyes, K.M., Mann, J.J., 2025. Addictive Screen Use Trajectories and Suicidal Behaviors, Suicidal Ideation, and Mental Health in US Youths. JAMA 334, 219–228. https://doi.org/10.1001/jama.2025.7829

Visuo-semantic dimensions in brains and AI models (Supervised by Kamila Jozwik)

Traditionally, vision neuroscientists have been using images of simple objects and focusing on one predefined object characteristic (e.g., shape, texture) or category (e.g., face, body). In our prior work, we analysed multiple dimensions at once and showed that different dimensions are important in different brain regions and at different times (Jozwik et al., 2016, 2018, 2023). However, the limitation of this work was that we may have missed some existing dimensions when predefining them. It is also unclear which of these dimensions are relevant to behaviour. Studies that attempted the characterisation of dimensions either did that only in the brain without any link to behaviour (Hebart et al., 2022) or focused only on very limited and arguably non-ecological behaviours (e.g., selecting which object is an odd one out in a triplet, Hebart et al., 2020).

We will extract dimensions in a data-driven way using brain responses to static images in two recent independent large-scale fMRI datasets (Hebart et al., 2022; Allen et al., 2022), focusing on brain regions involved in object recognition and semantics. Crucially, we will focus on dimensions that are relevant for behaviour (Krakauer et al., 2017) by running a range of real-life behavioural tasks. To understand the brain computations that lead to the emergence of the observed dimensions, we will use modelling with AI-based models (mostly deep neural networks, DNNs, current state-of-the-art models, Khaligh-Razavi et al., 2014; Schrimpf et al., 2018; St-Yves et al., 2022). By modelling, we mean extracting responses to images from these DNNs and correlating these DNN responses with brain and behavioural representations using representational similarity analysis. These analyses will test whether DNN models represent information in a similar way to the human brain and behaviour, ultimately contributing to understanding of computations in the brain and more human-aligned and safe AI models.

Exploring individual social media use with multimodal data (Supervised by Amy Orben)

Although social media use is highly individualized, we lack a systematic understanding of which personal characteristics describe and predict these unique patterns. This project investigates how dispositional, cognitive, affective, behavioral, physiological and sociodemographic factors jointly shape real-world social media behavior. The project will use multimodal datasets integrating survey measures (e.g. fear of missing out), experimental tasks (e.g. cognitive reflection), experience sampling (e.g. daily mood), passive smartphone tracking (e.g. app usage patterns), in-app behavior data (e.g. messaging and sharing), digital health indicators (e.g. steps and heart rate) and demographic information. By linking stable traits and dynamic states to high-resolution behavioral data, the project aims to develop a behaviorally grounded, integrative model of digital individuality and inform theory-driven, personalized digital interventions. Advanced statistical and potentially machine learning approaches will be applied to these data to identify individual profiles of social media use and connect them to predicting the effectiveness of presented digital interventions.

3. MRC DTP iCase PhD Studentships

* The application window for 2026-27 entry has closed for these studentships *

The School of Clinical Medicine has a number of MRC Doctoral Programme iCase PhD studentships, which are awarded competitively. Included in this advert is a project supervised by Amy Orben – ‘Exploring Digital Behavioural Data and Large-Scale Interventions to Enhance Digital Self-Control’. The application process is in line with standard CBU admission processes: applicants should apply for a PhD in Medical Research as usual, basing their research proposal on the advertised iCase project but demonstrating independent ideas and contributions that remain within the scope of the project.

Further information

If you require further information please get in touch via grad-admin [@] mrc-cbu.cam.ac.uk (without the brackets and spaces).

Please also see the University’s Postgraduate Study Events and Open Days webpages for information about the virtual open days taking place in November, as well as other events.

We also encourage you to try Unibuddy, which facilitates one-to-one chats with current Cambridge postgrads as a way to gain insight into study and student life in Cambridge.