The goal is to build models with improved predictivity for sample classification and improved interpretability to better understand molecular and cellular perturbations in common neurological disorders. The project will use new high-throughput data from patients, healthy controls, as well as in-vitro and in-vivo disease models. This will include the development of new structured machine learning approaches, the application of learning algorithms to complex biomedical data, and the joint interpretation of the data together with experimental collaborators. The candidate will conduct integrative machine learning analyses of cellular imaging datasets, with a focus on applications in neurodegenerative disease research. We seek a highly motivated machine learning scientist / computational biologist / bioinformatician (MSc level) who is experienced in applying statistical learning algorithms to large-scale biomedical datasets and optimizing and evaluating prediction models. The position is jointly with the lab of Enrico Glaab. New PhD student position with the topic "Machine Learning / Bioinformatics for Biomedical Datasets". #MITlinQ #Catalyst #Innovation #healthtech #PhD #appliedresearch Lizerbram Amar Mandavia Vimig Socrates Keir Warner Tom Rust, Enrique Gutierrez. Thanks to all mentors that are challenging and supporting us in this process Pranjul Shah Nancy Steele Martha Gray Benjamin Vakoc Indra Sandal, PhD, MBA Anne Quaadgras Timothy Padera Jacob Hooker Daniel Hurtado Melissa Parrillo and to the great fellows I get to work with Emily DeFraites, MD, MPH Javier Cubas Cano Lina Williamson Paul R. Honestly, I am humbled to be a part of this program and I'm particularly enthusiastic about what's coming in terms ofĢ) how to translate applied research into marketable solutionsĪs a PhD researcher on ML and bioinformatics, this experience is being very valuable, as it complements my academic studies and provides hands-on experience in the iterative process to identify and validate unmet needs, and most importantly: develop action plans. Additionally, it's a great space to connect with leading experts and be a part of the cutting-edge entrepreneurship and innovation ecosystem at #MIT. This program is an unparalleled opportunity for me to explore innovative and impactful solutions to unmet medical and healthcare needs. I recently joined the Massachusetts Institute of Technology MIT linQ Catalyst program 2023 cohort, and I'm excited to share the experience here and encourage others to apply for next editions! (PDF) Unravelling Inflammatory Pathways in Parkinson's Disease: Insights from Pathway-Based Machine Learning Analysis of Transcriptomics Data Julia Kessler Irina Balaur Henry Kurniawan Ziyun ZHOU University of Luxembourg If anyone from my network happens to be attending the program or is casually in Tokyo during this time, let me know - would be nice to meet up! I just arrived in Tokyo □□✈️ and will be here until the end of next week. I'm excited to be presenting my work on ML applied to functional representations of transcriptomics data in Parkinson's Disease, with a focus on neuroinflammation □□ Conference season is here again ! Hello from Tokyo! □□ □ □Ī few months ago I was awarded a grant from Luxembourg Centre for Systems Biomedicine (LCSB) - University of Luxembourg to attend the International Summer Program organized by RIKEN IMS and Tsinghua University (Institute for Immunology) and the RIKEN IMS-JSI International Symposium on Immunology 2023.
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