51 to 63 of 63 Permanent Bayesian Methods Jobs

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

a relevant engineering field. Experience with simulating qubits, quantum information, or superconducting electronics. Experience with running scientific software on high-performing computing systems. Experience in numerical optimization (e.g., Bayesian optimization, gradient-based optimization) Basic Qualifications for Sr. Principal Computational Physicist: PhD in theoretical physics, applied mathematics, or a relevant engineering STEM field plus 4 years of relevant … Proficiency writing code in Python. Mentorship and leadership experience in a scientific setting. Experience with running scientific software on high-performing computing systems. Experience in numerical optimization (e.g., Bayesian optimization, gradient-based optimization). This position requires the applicant to be a U.S. citizen. Must be able to obtain and maintain a security clearance per business requirements. More ❯

Jul 2 2025 As an Associate Director/Scientific Leader in the Protein Design and Informatics (PDI) team, you will focus on developing, integrating, and embedding bleeding edge computational methods and predictive in silico models that drive the discovery of new medicines and vaccines. Included in the larger Data, Automation, and Predictive Sciences (DAPS) department, we have an exciting … initiative aimed at researching and implementing new methods for the automation of the entire Design-Make-Test-Analyze cycle, impacting target discovery to the clinic - all stages of a therapeutic project. You'll have the opportunity to work in close partnership with many departments across GSK, developing and fostering a high-performing team culture of collaboration, curiosity, consistency, agility … Skills: If you have the following characteristics, it would be a plus Experience developing or applying modern ML architectures for protein design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.) Experience designing de novo binders for specified targets and epitopes Experience with cloud engineering production-ready robust and scalable scientific workflows Experience building and deploying More ❯

growing team focused on quantum sensing for a variety of industry sectors such as aerospace, defence, geophysical exploration and Earth observation. Perform essential research and development into performance-enhancing methods for next-generation fielded quantum magnetometers. Develop novel theoretical models and numerical simulation tools suitable for assessing the performance of quantum sensors in real-world environments. Develop platform and … discipline. Experience in the theory, numerical modelling, and/or optimization of one or more of the following disciplines: quantum atomic physics; quantum magnetometry; quantum control; real-time Bayesian estimation (e.g. particle filtering). Finally, you will have a strong desire to work with a world leading team and a company that is fundamentally building the future … quantum technology industry. It would be fantastic if you have these skills but not essential: Familiarity with the Python programming language for scientific computing. Experience in modern signal processing methods (e.g. Bayesian methods, particle filtering, sequential Monte Carlo methods); Experience in sensor modelling and sensor signal processing, including data fusion;Experience modelling optically-pumped More ❯

the GMASS program teams (e.g., Integrated Product Support, Depot Maintenance, etc.). This position is in Colorado Springs, Colorado. Job Duties: Develop or apply mathematical or statistical theory and methods to collet, organize, interpret, and summarize numerical data to provide usable information. Develop scripts and tools to automate data quality checks and support ongoing data hygiene within Power BI. … Apply statistical methods like Weibull, Bayesian analysis, MTBF estimations, Monte Carlo simulations (if required), and regression analysis - ideally in a reliability engineering context. No relocation assistance available with this position. Qualifications Bachelor's degree and 5+ years of professional experience. Must have an active Secret clearance. Experience with Python, Statistics and Power BI. Desired: Educational background More ❯

importantly, impact patients' lives. Opportunity We're now hiring a Director of Statistical Genetics & Functional Genomics to lead a growing team that integrates human genetics, functional genomics, and computational methods to drive novel target discovery. This is a rare opportunity to help shape our therapeutic pipeline by unlocking the causal mechanisms of disease from human data. Your responsibilities Lead … architecture. Collaborate cross-functionally with disease biology, target discovery, and experimental teams to translate genetic insights into testable hypotheses and novel therapeutic concepts. Stay at the forefront of emerging methods in statistical genetics and variant-to-function inference, guiding internal adoption and development. Contribute to platform strategy and target identification efforts across multiple disease areas. Foster a culture of … and supporting their continued development. Who you are You bring deep expertise in human statistical genetics, including fine-mapping, LD structure, polygenic risk scoring, partitioned heritability, and causal inference methods (e.g. Mendelian Randomisation). You have experience integrating multi-modal data-including GWAS, transcriptomic (bulk and single-cell), epigenomic (ATAC-seq, ChIP-seq), and proteomic datasets-to identify causal More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

General Theory for Big Bayes" and Grenoble IDEX. Interested applicants should write to us with: a letter of interest, CV, and should require two recommendation letters. Context Bayesian deep learning brings together two of the most important machine learning paradigms: Bayesian inference and deep learning. On the one hand, Bayesian learning … theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art The field of machine learning has recently been much impacted by … processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesian statistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory More ❯

STD 464 Preferred Requirements Experience with Low Observables Basic Radar Signal processing Able to read and interpret Government requirements Electromagnetic Airworthiness Mil-Std 461, 464, 516 HEMP testing Bayesian statistics Desire and ability to learn technical challenging topics Ability to communicate well with PhD's and leadership Excel and Word skills The listed salary range for this More ❯

experiment analysis techniques, including Bayesian and Causal Inference. Modeling Skills: Experience working on advanced modelling concepts. SQL and Statistical Analysis: Strong proficiency in SQL and statistical methods for analyzing complex data sets. Proven experience in conducting impact analysis to assess viable opportunities and scoping their relevance. Track Record: A successful track record of managing complex projects More ❯