********************Jobs******************** The School of Biological Sciences at the University of Bristol is advertising three Lecturer positions (equivalent to Assistant Professor). We would be delighted to welcome new colleagues to join our collaborative community. For details on the position and the application process see: https://www.bristol.ac.uk/jobs/find/details/?jobId=385935&jobTitle=Lecturer%20in%20Biological%20Sciences Deadline: 8 March 2026 Please share this opportunity with anyone seeking a permanent academic position. Beatriz Gon�alves Senior Lecturer School of Biological Sciences University of Bristol Beatriz Goncalves (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca) ********************PostDocs******************** Postdoctoral Fellow in Virus Evolution and Spillover Organization National Library of Medicine, Bethesda, MD and surrounding area The Division of Intramural Research (DIR) at the National Library of Medicine (NLM) invites applications for a position as a Postdoctoral Fellow supervised by Dr. Martha Nelson. About the position The NLM is one of the 27 institutes at the National Institutes of Health (NIH). The NLM is the world�s largest biomedical library and a leader in research, development, and training in biomedical informatics and health information technology. The DIR within the NLM has two primary research areas: computational health research and computational biology. In computational health research, our efforts center on natural language processing (NLP), clinical image analysis, biomedical ontologies, information modeling, and clinical data analytics. In computational biology, we emphasize transcriptional regulation, chromatin and network biology, structural and functional analysis, sequence statistics, and evolutionary genomics. The post-doctoral scholar will study how rapidly evolving RNA viruses (e.g., H5N1 avian influenza, coronavirus) transmit and evolve at the human-animal interface, using advanced Bayesian phylodynamic approaches and large-scale genomic data. The post-doctoral scholar will perform data analyses within an interdisciplinary team of international scientists who conduct fieldwork, experimental studies, and statistical analyses on emerging pathogens. This position is strictly computational (no fieldwork or lab work required). This project is part of a longstanding government-academic partnership spanning multiple Federal agencies and academic institutions to study disease spillover between humans, wildlife, and livestock and identify successful intervention strategies to break transmission in real-world settings. Position Overview: This is a full-time postdoctoral fellow position. The initial appointment will be for one year, and is renewable on a yearly basis, with extensions up to 5 years total. The NIH offers a competitive salary (based on postdoctoral experience, see stipend tables: https://www.training.nih.gov/stipends/) and comprehensive health insurance. The NIH is dedicated to the continued education and career development of all its research staff. Candidates are subject to a background investigation. Additional information about NIH postdoctoral fellowships: https://www.training.nih.gov/research-training/pd/ Apply for this vacancy What you'll need to apply Prospective candidates should include "Post-doctoral Inquiry" and their last name in the email subject line. Applicants must submit the following materials to Dr. Martha Nelson at nelsonma@mail.nih.gov. * Updated CV * Statement of research interest * Contact information for 3 references (please include the full name with titles, institute, email address and phone number of each reference). Application Deadline: Applications will be accepted until the position is filled. Contact name Dr. Martha Nelson Contact email nelsonma@mail.nih.gov Qualifications * Candidates should have or be close to obtaining a Ph.D. or equivalent degree in computational biology, computer science, bioinformatics, molecular biology, or a closely related field. * Candidates with experience using Bayesian approaches to phylodynamic analysis of virus populations (specifically BEAST software) are particularly encouraged to apply. * Fluency in R, python, or another programming language is essential. * Experience working with genomic data from pathogens (especially RNA viruses, e.g., influenza A virus) is essential. * Applicants must possess good communication skills and be prepared to work collaboratively on a fast-paced team of international scientists to inform time-sensitive outbreak responses. Disclaimer/Fine Print U.S. citizens and permanent residents are eligible to apply. NIH welcomes foreign nationals with the exception of individuals from this list . "Nelson, Martha (NIH/NLM) [E]" (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca) ********************WorkshopsCourses******************** Machine Learning for Time Series using Python -Online recordings and live Q&A This intensive five-day online course provides a practical introduction to machine learning techniques for modelling, analysing, and forecasting time-series data using Python. Designed for researchers, analysts, and data scientists who work with temporal datasets, the course focuses on hands-on workflows that can be applied directly to real-world problems. Participants will learn how to prepare and visualise time-series data, implement machine learning models for forecasting and classification, evaluate model performance, and interpret results in a statistically sound way. The training combines structured teaching with guided exercises, giving attendees experience with modern Python tools and methods commonly used in time-series analysis. All sessions are delivered live online and recorded for later review, with opportunities for questions and discussion during dedicated Q&A sessions. Course details Dates: 11-15 May 2026 Format: Online recordings and live Q&A Fee: �450 This course is suitable for professionals and postgraduate researchers who have basic familiarity with Python or data analysis and want to develop practical machine learning skills for time-dependent data. Register here: https://prstats.org/course/machine-learning-for-time-series-mltp01/ Oliver Hooker PhD. PR stats Oliver Hooker (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca)