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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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Drive cross-functional collaboration across our data science, machine learning, lab informatics, and medicinal chemistry teams to advance our computational infrastructure for our drug discovery and development mission.
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PhD in computer science, applied mathematics, physics, computational biology or other quantitative disciplines. The ideal candidate has a machine learning background, with previous experience building large-scale generative models, complex pre-processing pipelines, and comprehensive benchmarks to support scientific tasks.
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Key medicinal chemistry principles to your cross-disciplinary colleagues from Computational Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology. At 1910 Genetics, we put computation at the heart of drugdiscovery, blending expertise in computational chemistry, structuralbiology, pharmacology, genetics, data science, and softwareengineering to develop drugs for previously undruggable targets.
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PhD. or M.S in computational sciences (e.g. computer science, physics, mathematics, computational biology) preferably with applications in biology. Ampersand Biomedicines, Inc is looking for an enthusiastic and self-driven AI/ML Engineer with deep expertise in development of AI/Machine Learning/Deep Learning to join our team in developing a cutting-edge platform to identify and design new multi-specific medicines across a spectrum of therapeutic areas.
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As an ML Engineer, you will lead the development and deployment of innovative approaches to optimize existing and new machine learning systems, unlocking their full potential to drive maximum business value.
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The Algorithms, Machine Learning, & Data Science Group within AES Advanced Tech Team creates advanced algorithms in the fields of machine learning, artificial intelligence, signal processing, intelligent control, and other areas of electrical engineering and computer science that are of strategic interest to the AES Business Unit’s goals and roadmaps.
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PhD or MSc in computational biophysics, computational chemistry, chemical engineering, computer science, machine learning or related field. We are seeking a highly motivated machine learning scientist to join the Chemical Data Science team.
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The team leverages its experience in the fields of signal processing, computer vision, computational linguistics, machine learning, artificial intelligence, communication systems, and other areas of electrical engineering and computer science to create impactful solutions.
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We encourage individuals with academic teaching experience (high school or higher education), ed-tech experience from start-ups to larger organizations, and those with direct experience in educational publishing to apply, among others whose creativity and/or expertise will lead to success in working with a team to develop teaching and learning strategies for digital learning with an initial focus on AI-supported pedagogical tools.
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Work at the intersection of experimental and groundbreaking digital technologies, with a particular emphasis on expertise in machine learning and artificial intelligence (AI) as applied to small molecule drug discovery.
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There are various disciplines involved in Enterprise Data & Analytics, including: data source identification and analysis, data engineering, data visualization & data science/machine learning.
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Strong understanding of fundamental computer science concepts, software design best practices, software development lifecycle and common machine learning design patterns. Extensive experience in at least one cloud platform (e.g. AWS, GCP, Azure) and associated machine learning services, e.g. Amazon SageMaker, Azure ML, Databricks.
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MS or PhD in Computer Science, Electrical Engineering, or relevant field and/ordemonstratedexcellence in relevant technical domains throughopen sourcecontributions or publications.
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PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. PhD focus on NLP or Masters with 5 years of industrial NLP research experience Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization) Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Publications in deep learning theory Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR.
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machine learning technical lead computer science jobs Company: Jpmorgan Chase Co in Boston, MA
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Recruiting in a Recession: Hard Truths That Talent Acquisition Experts Must Accept
The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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We’ve all heard how important it is to set professional and personal goals. Developing and establishing goals keeps us motivated and moving forward in life. But not all goals are created equal. If you’re chasing goals that are too lofty, you’ll end up disappointed when you cannot reach them. Setting goals that are achievable and measurable is the key to success.
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"Nothing we do is more important than hiring and developing people."
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Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!
4 Talent Acquisition Trends Going Into 2023
For better or worse, a side effect of the COVID-19 pandemic was a marked shift in talent acquisition practices worldwide. With the struggle to retain talent that began in 2020, companies have had to rethink recruitment strategies. The result has been new talent acquisition trends that are well on their way to becoming commonplace. These are the practices that are going to become even more widespread going into 2023.