Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- University of Birmingham
- Nature Careers
- King's College London
- UNIVERSITY OF SOUTHAMPTON
- University of Nottingham
- Queen's University Belfast
- The University of Southampton
- KINGS COLLEGE LONDON
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- UNIVERSITY OF MELBOURNE
- University of Glasgow
- University of Sheffield
- University of Surrey
- ; University of Exeter
- Birmingham City University
- Imperial College London
- Manchester Metropolitan University
- Nottingham Trent University
- Queen's University Belfast;
- UCL;
- UNIVERSITY OF SURREY
- University College London
- University of London
- 14 more »
- « less
-
Field
-
especially LLMs, evidenced by publications and/or dissertation or equivalent evidence of expertise and completed research outputs Proven ability to write code in Python Experience working in a research team
-
. Candidates must also have excellent programming skills in a relevant scientific language, such as Mathematica or Python, to facilitate advanced data analysis of measurement outputs. Apart from being able
-
research outputs Data science skills, especially data analysis and prediction modelling Proven ability to write code in Python Experience working in a research team Excellent writing and communication skills
-
, bioinformatics or related discipline Strong computational skills, with expertise in scripting in BASH and either R or Python Experience with analysing complex datasets on high-performance compute clusters A track
-
and commercial tools (e.g. XCMS, MZmine, Compound Discoverer, GNPS, SIRIUS, etc). Proficiency in one or more programming languages (e.g. R, Python). Experience with continuous integration and best
-
. neuroimaging, time-series signals, clinical records) for predictive modelling and decision support. Proficiency in programming languages such as Python (and/or Java, C/C++), with hands-on experience using AI
-
. Possess relevant hands-on experience in Python, Java, C++ etc. and corresponding development environments.
-
relevant analysis methodologies. Essential Application/Interview Experience of quantitative data analysis using appropriate software (e.g., R, Stata, Python). Essential Application Dissemination of research
-
. Strong experience with Python and at least one deep learning framework such as PyTorch, PyTorch Lightning, TensorFlow or JAX. Familiarity with packages and technologies such as NumPy, Pandas, Scikit-learn
-
quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential Experience in applying computational methods to research