Duties: We are looking for a Postdoctoral Researcher in Computational Statistics to join our Statistics and Biodemography research group. Our group specialises in developing statistical and mathematical theory, and scalable computational methods, crossing boundaries between evolutionary ecology, social data science, and genomics. This post will be connected to a BBSRC-funded research project developing methodology for deep phenotyping based on latent stochastic processes, with applications in human health, genomics, and ageing. Our goal is to provide statistical tools that can keep up with the explosive growth in the availability of high-dimensional genomic and longitudinal data from electronic health records, cohort studies, and biobanks to improve risk prediction, the allocation of health resources, and our fundamental understanding of ageing
Requirements: The candidate will hold, or be close to completion of, a relevant PhD/DPhil and have a demonstrated high-quality publication record. There is some flexibility to shape the role around the expertise and interests of the person hired. Experience with stochastic modelling, deep learning, software engineering, high-dimensional time series, statistical genetics, or event-time analysis would be helpful, but not essential. Ability to solve problems in a collaborative environment is essential

Duration: 1 y       
Workload: 100%
Duties: The School of Mathematics at the University of East Anglia (UEA) wishes to appoint a Senior Research Associate (SRA), whose role will be to work within the UKRI-EPSRC-funded research programme of Professor Shaun Stevens on the local theta correspondence. The SRA will prepare and present research findings to peers, at conferences, to funders and by publication in high-quality journals. The SRA may also take on a small amount of teaching
Requirements: The Research Associate will be educated to PhD level (or equivalent) in mathematics, with strong experience in the local Langlands and/or theta correspondence for p-adic groups. As well as possessing a strong subject background, the appointee will have proven self-motivated research experience, with an ability to work independently as well as under guidance. The ability to plan one’s own research is essential, as are strong written and verbal communication skills. You must also be able to fulfil all essential elements of the person specification

Duties: Optimization of collateral and inventory under various constraints arising from regulatory, contractual, capital, etc. requirements on one hand and expected duration, internal opportunities, etc. on the other; Develop mathematical/statistical models for Prime desks to enhance business revenue and profitability for stock borrow-loan, cash and synthetic financing books; Devise solutions for systematic book management, improving the overall stability of our collateral and its respective uses; Deliver quantitative analytics to the desks that drive decision making; Maintain adequate control functionality
Requirements: Advanced degree (Masters, PhD, or equivalent) in Math, Sciences, Engineering or Computer Science; Significant work experience in a related field; Analytical, quantitative and problem-solving skills; Excellent communication and presentation skills, in particular to senior stakeholders; Understanding of statistics and machine learning methodologies; Software design and development skills, particularly in Python

Duties: The successful candidate will be joining the Portable Atomic Clock team in the ITCM group. Their main focus of work will be updating the current sub-systems to take full advantage of the latest advances in laser and fibre technology, and to integrate them into a fully functioning portable optical clock. Focus will be on the system’s performance and manufacturability, as well as developing and optimising novel spectroscopy schemes to improve the clock’s stability and accuracy
Requirements: A PhD or equivalent scholarly or relevant professional activity; Pursuing a line of independent research within a research group; Publishing research (either from a recently completed PhD or new original research)

Duties: Developing validation tools to ensure the internal model meets the necessary Solvency 2 standards; Producing models to replicate or sensitivity test using MATLAB/R/Python; Producing validation reports on components of the internal model; Reviewing of regulatory submission to the PRA related to the model; Monitor the sufficiency of premiums and the adequacy of pricing frameworks, pricing models and pricing controls; Review of reinsurance contracts and reporting on the adequacy of reinsurance arrangements; Review and provide validation opinions on the appropriateness of demographic, expense and other technical provision assumptions; Validate the technical provisions within Solvency 2 regulatory reporting, including the matching adjustment and transitional measures
Requirements: Qualified Actuary or senior student; Previous experience in a number of actuarial roles including valuation, capital management, internal model, ALM, experience monitoring; Demonstrated technical expertise in at least two different areas of actuarial work; Ability to apply risk management techniques to quantify and manage financial risks; Experience of relevant modelling software such as MATLAB, Python or R; Strong technical skills and knowledge of Solvency 2; Ability to practically apply good risk management to distinguish material errors and issues from trivial issues

Department: Computer Science
Workload: 100%
Duties: The Department of Computer Science is looking to recruit a researcher in Machine learning methods for digital epidemiology. This position is funded by the EPSRC project i-sense and an unrestricted Google Gift. The successful candidate should have the desire and ability to: (a) develop machine learning methods for digital/computational epidemiology with a focus on disease prevalence forecasting using neural network architectures that are able to leverage non-medical information such as web search activity, (b) constraint models developed in (a) with priors from established epidemiological approaches (e.g. mechanistic models) in joint optimisation frameworks, (c) publish the outcomes in peer-reviewed conferences or journals, and (d) deploy such models to forecast influenza and COVID-19 rates in the UK (at least) and share outcomes with the UK Health Security Agency
Requirements: The successful candidate should have a PhD in a relevant subject (or should at least have submitted their PhD Thesis), with strong experience in machine learning, proven by a strong publication track in related conference venues and journals. Previous experience in health-related topics is a desirable skill

Department: UCL Queen Square Institute of Neurology - UK Dementia Research Institute at UCL
Workload: 100%
Duties: You’ll examine the effects of traumatic brain injury on neuronal circuit and neurovascular function in vivo, recording neuronal activity and vascular dynamics using state of the art two-photon and electrophysiological (Neuropixels) methods and linking this to available human datasets. This is fantastic chance to work independently on a high impact, state-of-the-art collaborative and cross-species project in a stimulating and vibrant research environment
Requirements: You will have a PhD (or be near to completion) or equivalent in Neuroscience, or a neuroscience related quantitative discipline and a strong background in cellular circuit and/or systems neuroscience. Practical experience with Neuropixels probes or other high-density extracellular recordings probes and/or in vivo two-photon calcium imaging is essential, as is practical understanding of modern behavioural assays and associated hardware and software, and experience working with live mice and of rodent stereotactic surgery. Strong programming and coding ability (e.g., MATLAB or Python), outstanding analytical and/or statistical analysis skills and a strong publication record are also key requirements for this role

Duties: The post holder’s main duties and responsibilities will include undertaking innovative research as part of a team of researchers working towards the goals of the CoSInES project. The work will involve at least some of the following areas: Machine Learning, Computational Statistics, High-Performance Computing, Theory and Methodology of stochastic algorithms, Scalable methods. Other responsibilities include managing own academic research and administrative activities, including small scale project management
Requirements: Applicants are sought who hold or is close to the completion of a PhD/DPhil in Machine Learning, Statistics or closely related discipline. The successful candidate will have experience of programming in Python or equivalent languages, a demonstrable ability to publish, including experience of producing high-quality academic writing and previous research experience relevant to the research programme

Duties: We are looking to recruit a high calibre researcher and a data analyst to our Standards & Comparability and Data & Analytics teams. Research and data are at the heart of our approach to intelligent, evidence-based regulation. The work we do strengthens our regulatory decision-making, supporting our position as a credible authority by adopting innovative, critical approaches to research and business intelligence. Our research has a direct impact on education, informing approaches to the assessment and maintenance of standards in vocational and national qualifications
Requirements: A degree (preferably a higher degree) in a subject with a significant quantitative element

Workload: 40%
Duties: Applications are invited for an enthusiastic Research Fellow - Biostatistician, who have an MSc in either medical statistics or epidemiology and ideally have a strong background in longitudinal clinical data analysis, to join the CureME Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research group at Clinical Research department, Faculty of Infectious & Tropical Diseases, at the London School of Hygiene & Tropical Medicine (LSHTM). The post-holder will be joining the CureME research group, to help with longitudinal data analysis, including clinical and lab-based derived data on consenting research participants with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and control groups, from the UK ME/CFS Biobank (UKMEB). Alongside this, there will be opportunities to contribute to grant applications, including high profile epidemiological studies using electronic health record data
Requirements: The successful candidate will have a postgraduate degree, ideally a doctoral degree in medical statistics and epidemiology, relevant experience in conducting epidemiology/statistical research, particularly on longitudinal (cohort) studies, proven ability to work independently, as well as collaboratively as part of a research team, and proven ability to meet research deadlines, evidence of excellent interpersonal skills, including the ability to communicate effectively both orally and in writing. Further particulars are included in the job description

Duties: Undertaking discrete small research projects/tasks, e.g. the development of statistical analyses and take responsibility for specific elements within larger projects under the supervision of senior staff; Assist with preparing datasets for analysis in line with the statistical analysis plans and to undertake analyses as detailed in an analysis plan; Assist with the preparation of study protocols, including statistical analysis plans, specifying table shells and figures as required; Assist with quality assurance review of statistical models developed both within the company and for external clients; Involvement in preparing proposals for project work, e.g. background research, gathering literature, drafting/collating standard sections of proposal
Requirements: Educated to degree level in medical statistics, statistics, epidemiology, health economics, econometrics, mathematics, or a similar related quantitative discipline; Good educational background (including English and Mathematics GCSE grade B or equivalent); Experience of undertaking and or contributing to quantitative research projects; Good IT skills in Excel including basic knowledge of functions

Department: UCL Great Ormond Street Institute of Child Health
Duties: This position involves working as part of a multidisciplinary consortium investigating epidemic and pandemic norovirus spread through a wide range of experimental and computational approaches including whole genome sequencing, environmental, patient, and community surveillance, biochemistry and immunology of biological and genetically manipulated samples and epidemiological modelling. In addition, he/she will be able to collaborate with the community of bioinformaticians and computational biologists within UCL Genomics and ICH. Candidates will be expected to help supervise the day-to-day activity of a current PhD student working on the grant
Requirements: Candidates should have strong programming skills with a PhD in bioinformatics, evolutionary biology, mathematical modelling, computational biology, statistics or related genomic disciplines and should have prior experience with next-generation sequencing data, translational bioinformatics, data mining of large-scale genomic datasets and databases

Duties: Build rules, algorithms and machine learning models, to respond to and mitigate business risks in ByteDance products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc; Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries; Define risk control measurements. Quantify, generalize and monitor risk related business and operational metrics. Align risk teams and their stakeholders on risk control numeric goals, promote impact-oriented, data-driven data science practices for risks
Requirements: Bachelor or degrees above in computer science, statistics, math, internet security or other relevant STEM majors (e.g. finance if applying for financial fraud roles); Proficiency in data science analytical tools, such as SQL, R and Python; 4+ years of strong industry experience in relevant data mining domains. Example topics include (not limit to): search and ranking, recommendation, ads, anti-fraud/abuse, financial risk control. Good at telling data stories

Duties: Design and develop data science models and solutions using modern software techniques and tools; Work within agile squads in a multi-disciplinary; Understanding of methodologies and approaches to sufficiently explain trade-offs, nuances of approach(es) to technical and lay person audience; Work closely with the data community to build capabilities, to mentor and influence more broadly the importance of data science; Source, determine and assess data availability and quality as to whether fit for purpose and propose steps if not; Communication skills to articulate how problems can be solve with data driven approach, build confidence in proposal and solution, create narrative, provide clarity and comprehension of solution
Requirements: Masters in applied maths subject e.g. Data Science/AI, Statistics, Computer Science, Big Data, Engineering, Physics, Economics, Maths; Experience of working in a business context; Experience of developing and deploying statistical and machine learning models (supervised and unsupervised) that have delivered results e.g. retention model, customer segmentation, next best actions; Essential - Python or R, SQL, Big Data, BI tool (e.g. Tableau, PowerBI, QuickSight, Qlik); Modern software development experience and toolkit e.g. Git, code reviews, Data Science CookieCutter, Environments, Docker, CI/CD

Department: Dept of Medical Physics & Biomedical Engineering/Cancer Institute
Workload: 100%
Duties: This post is for a Lecturer (assistant professor) in computational pathology to join a new team which includes pathologists, computational scientists, clinician scientists, medical physicists and biologists. We are looking for an outstanding candidate to develop an internationally leading programme of research in the field. In terms of teaching, the Department of Medical Physics and Biomedical Engineering offers a number of degree programmes, including a Medical Physics BSc/MSci programme in partnership with the UCL Department of Physics & Astronomy, an MSc in Physics & Engineering in Medicine, an MSc in Medical Robotics and AI and a BEng/MEng programme in Biomedical Engineering. The Cancer Institute offers an MSc in Cancer, a BSc in Cancer Biomedicine and an iBSc in Oncology
Requirements: Candidates must have a PhD in a relevant subject, with a considerable computational component and track record of research in computation applied to medical/biological areas. A track record of publishing impactful research in leading international journals, with relevant post-doctoral experience along with Experience of grant writing/research funding. The successful candidate will have exceptional computational skills, including artificial intelligence, and a highly competitive track record in applying computational methodologies to medical/biological problems. A good working knowledge of cancer and/or pathology is preferred

Department: UCL Physics and Astronomy - Atomic, Molecular, Optical and Positron Physics
Duration: 3.5 y
Duties: This project will involve the theoretical formulation and computational implementation of state-of-the-art semi-classical techniques. Moreover, it will be performed in collaboration with world-leading experimental groups in Attoscience as well as with world leading theorists
Requirements: The successful applicant should expect to achieve a 1 degree in Physics. Candidates should also have excellent written and verbal communication

Duties: This project aims at developing an automated algorithm for stratigraphic correlation and deploying the first graphical user interface (GUI) to perform probabilistic alignment of palaeoclimate records. The successful applicant will: 1) develop an improved Markov Chain Monte Carlo (MCMC) alignment algorithm that models alignments based on multiple proxy signals and incorporates prior knowledge on the depositional history of the climate archives used for correlation; 2) design a dedicated GUI software to facilitate the usability of the algorithm. The new algorithm and related GUI will provide an essential tool to construct robust chronologies for climate archives with poor independent age control and will increase the accessibility of probabilistic alignment methods to the wide palaeoclimate community
Requirements: Eligible candidates must have a PhD in Earth Science, Geological Science, Applied Mathematics, Computer Science, or similar. Strong programming skills and prior research experience in MCMC techniques and Bayesian inverse modelling would be a significant advantage. Applicants must also have proven experience of publishing high-quality research articles. They must be highly motivated and should have excellent organisational and communication skills, and be able to work well as part of a team

Workload: 100%
Duties: Conduct research in the field of AI and develop novel methods and algorithms to improve the capabilities and scalability of our technology platforms and drug discovery processes; Design, develop, and optimize ML models around large scale microscopy and other visual datasets to enable better phenotypic screening and drug development; Improve and optimize ML techniques to achieve better model performance; Work with other research scientists within the organization and together develop novel methods that combine cutting edge AI with expert domain knowledge; Stay informed on the latest research in relevant scientific fields and analyze its potential impact on our capabilities; Interact with the larger AI communities by attending conferences or workshops and writing high-impact publications
Requirements: Master’s or PhD degree in artificial intelligence, machine learning, mathematics, physics, computer science or a related field with a comparable focus on analytical skills. Or an undergraduate degree in any one of these fields combined with comparable research experience from a professional career; Previous background in computer vision; Good understanding of statistics and how statistical concepts are applied in machine learning; Ability to conduct novel research in a field of artificial intelligence such as machine learning or computer vision; Advanced proficiency in at least one programming language that is commonly used in scientific computing and machine learning (preferably Python or R)

Department: Department of Mathematics
Workload: 100%
Duties: Applications are invited for two full-time Research Fellow positions in the analysis group at UCL. This project focuses on problems in in spectral and scattering geometry, especially those arising from high energy spectral asymptotics and from the Steklov problem. The project focuses on the study of spectral asymptotics both in the setting of scattering theory and compact manifolds using microlocal methods. The responsibilities of the posts will include: carrying out vigorous, high-level research in the area of analysis, submitting publications regularly in refereed journals; participating in local seminars and in national and international conferences
Requirements: Candidates should either have a PhD or equivalent qualification in mathematics, or should be finalising their PhD or waiting for their viva date; The successful candidates are expected to have a proven track record in research and publishing, excellent grasp of analysis and geometry. As well as excellent interpersonal skills to facilitate participation in a research group, and excellent communication skills, both written and oral. An outstanding candidate will show research experience in analysis and geometry, especially spectral and scattering geometry

Duties: For 2022 entry, LSE will be offering approximately 80 studentships to new PhD students in the form of LSE PhD Studentships, LSE DTP ESRC Studentships and London Arts and Humanities Partnership (LAHP) Studentships
Requirements: The awards are open to high calibre students of all nationalities studying across all research areas at the School but, in particular, we are seeking to support applications from: UK students, Women whose research will be within the field of Economics, Finance, Accounting, Management, Mathematics, Statistics and Methodology

Duties: Allocation of sources of profit and loss to financial reported results, by driver; Provides improved insight to shareholders and supports external reporting; Accountable for elements of UK Life financial reporting. Requires close collaboration and engagement with stakeholders across other finance areas including financial management, finance business partnering and asset origination; Responsible for delivery of results on a monthly basis, using flexible actuarial resource (1-2 colleagues) from actuarial production and reporting teams to support delivery; Required to consult on and make judgements in presentation of financial results; Presentation of results regularly to senior UK Life stakeholders; Planning ahead is necessary for the next 12-18 months, covering monthly, quarterly and annual reporting cycles
Requirements: Strong understanding of financial reporting metrics including IFRS and Solvency II; Good knowledge of IFRS17; Ideally degree qualified, Fellow of the Institute and Faculty of Actuaries

Duties: Research and development of technologies on machine learning fairness and ethics for products (including but not limited to machine learning interpretability, robustness, and privacy); Conduct research on machine learning fairness and collaborate with experts in related fields in academia and industry; Carry out design and theoretical analysis of algorithms, and apply them to recommendation, search, CV, NLP and other fields
Requirements: Researchers or PhD graduates in machine learning and other related fields; In-depth understanding of machine learning with rich experiences in relevant fields; Solid mathematical background and coding ability; Publications at the top conferences is a big plus

Duties: This fully funded PhD studentship represents a unique opportunity to undertake current density measurement and modelling research in close partnership with Paragraf Ltd. The successful candidate will be embedded within a multidisciplinary academic team, working in close partnership with the industrial sponsor. The student will have access to the unique experimental facilities contained within the WMG Energy Innovation Centre to advance their research. The research will address the fundamental knowledge required for the current density measurement of lithium-ion battery
Requirements: Prospective candidates are expected to have a minimum 2.1 undergraduate (BEng, MEng, BSc, MSci) and/or postgraduate masters’ qualification (MSc) with 65% or above. Experimental design, data processing, mathematical modelling, electrical or electrochemical engineering. An underpinning interest in battery and sustainable mobility will be an advantage

Duties: X-ray imaging (X-ray computed tomography, XCT) is used across manufacturing such as aerospace and automotive to identify defects in industrial components and measurement. The technique can provide rich information on internal geometries that are otherwise inaccessible which is its greatest allure, particularly with additive manufacture. But the scanning process can take 30 minutes to a number of hours depending on the part meaning it is only suitable for high-value low-volume applications
Requirements: Prospective candidates are expected to have a minimum 2.1 undergraduate (BEng, MEng, BSc, MSci) and/or postgraduate masters’ qualification (MSc) with 65% or above. The project is suitable for physics, computer science, engineering, or mathematics graduates with strong computational skills

Duties: In this project, you will develop earthquake forecast models in large scale based on physics-based simulations and/or AI-based earthquake forecasts, both aiming to improve our process-based understanding of earthquake triggering. Those large-scale processes may inform us about future experiments in the lab motivating further rock physics research. The framework will generate, evaluate, optimize and discriminate earthquake forecasts based on robust statistical modelling and validation
Requirements: A completed research-oriented university degree, such as a Master's degree or BSc Hons, in a relevant field (e.g. Geophysics, Physics ); An outstanding academic track record; A good command of English, both verbal and written; Knowledge of patterns of crustal deformation and experience in earthquake catalog analysis are considered a plus; Knowledge of computer programming languages, such as Python, and other essential skills towards machine-learning model development (e.g. Tensorflow, ELK) will be considered a plus