Research Fellow (Training Fellow in Theoretical Neuroscience)

UCL Eastman Dental Institute
London
11 months ago
Applications closed

Related Jobs

View all jobs

Data science programme lead

Data science programme lead

Technical Program Manager - Machine Learning - New York

Data Scientist - National Security (TIRE) based in Cheltenham/Hybrid

Senior Data Scientist - National Security (TIRE) based in Cheltenham/Hybrid

Data science programme lead

About the role

We are now inviting applications for a post-doctoral training Fellowship under the guidance of Dr Agostina Palmigiano, focussed ondeveloping theoretical approaches to investigate the mechanisms underlying sensory, motor or cognitive computations.

You will be responsible for the primary execution of the project (with opportunities for co-supervision of students), presentation of results at conferences and seminars, and publication in suitable media.

This post is initially funded for 2 years with the possibility of a one-year extension at the end of the period. 

A job description and person specification can be accessed at the bottom of this page.

About you

You should have a strong quantitative background in theoretical neuroscience, machine learning, statistics, computer science, physics or engineering; a record of publication in highly respected journals and conferences and must hold a PhD in a relevant field by the agreed start date of the position.

To apply, please click Apply Now and submit your CV and in the Attachments section (Research Paper 1) a statement covering research accomplishments and the names of 2 referees. There is no requirement to upload any papers you have authored. Academic enquiries about the role should be directed to Agostina Palmigiano ().

Applications will be reviewed on a rolling basis until the position is filled. Early applications are strongly encouraged, as this ensures they will receive full consideration. 

What we offer

The Gatsby Unit offers competitive salaries and an award-winning work environment. You will work in a vibrant, interactive and collaborative environment, with world-class PhD programmes, generous core funding and travel allowances. Our facilities include an on-site high-performance computer platform, an extensive seminar programme and interaction space, an on-site brasserie, and outdoor spaces. Our staff are entitled to UCL's extensive range of staff benefits, including a generous annual leave entitlement, family-friendly policies, occupational pension schemes, relocation and housing assistance (where applicable) and professional development opportunities.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.