Company Logo of BIOS in Cambridge UK which uses neural interfaces & AI - Neural Signals

Summer 2021 Internship Opportunity – Machine Learning and AI

 

SUMMER 2021 INTERNSHIP OPPORTUNITY IN MONTREAL, CANADA

 

About this internship:

We are seeking two interns to participate in a collaborative project involving BIOS Health, McGill, and Université de Montreal. The project, entitled “Development of an AI-controlled closed-loop neuromodulation system for chronic conditions,” is primarily funded by MEDTEQ. The internship portion is co-financed by BIOS and Mitacs, which means that candidates must be enrolled at a Canadian university that will formally employ them. Limited to Masters, PhD, and Post-docs. Successful candidates will be supervised by Prof. Blake Richards (McGill) and/or Prof. Guillaume Lajoie (Université de Montréal).

 

About BIOS:

BIOS is unlocking the potential of the nervous system in treating chronic disease by using AI-powered neural interfaces that can automatically read and write neural signals. The human nervous system carries vast quantities of data and scientists have long known that faulty signals in the nervous system play a key role in driving chronic diseases. By understanding and correcting these signals in real time, BIOS can treat chronic illnesses in an effective, automated, and personalized way, leading to seamlessly experienced healthcare. BIOS utilizes its proprietary breakthroughs in AI and Machine Learning to translate the “language” of the nervous system for the first time. BIOS’ neural code is built on the world’s largest proprietary neural data set and is already in use clinically to enhance data from wearables used in remote chronic disease care.

BIOS is made up of a wide range of experts from neuroscience, machine learning, software engineering, applied biomaterials, biotechnology, medicine, and business and operations. The combined experience of the BIOS team extends to over 300 peer-reviewed publications, 10+ first of kind medical devices, and 6k+ clinical procedures.

 

We are looking for people who will fit into our diverse, fun and stimulating company team culture, who are focused on overcoming challenges, and want to grow along with the company. We are especially looking for candidates that share our values: enjoy solving hard problems; bring a unique perspective but become an active part of the BIOS team; respect what matters and do what’s right; and enjoy the challenge.

 

About the role:

We are looking for candidates who can skillfully apply machine learning in a signal processing context. Successful candidates will apply their strong coding skills to world-changing, completely unique projects for neural engineering applications.

 

Main duties and responsibilities:

  • Perform research to apply machine learning (particularly deep learning, Bayesian machine learning) to multivariate neurological datasets.
  • Evaluate and compare different machine learning strategies on complex unstructured multivariate time series data
  • Interact regularly with Mila researchers, BIOS’s data team and our data acquisition task force.
  • Evaluate and compare different machine learning strategies on different datasets
  • Identify critical components of source data and design optimal data acquisition paradigms

 

Essential skills and qualifications:

  • Proven knowledge of machine learning and/or statistics and demonstrated ability to apply to complex unstructured data
  • Previous experience conducting research projects within academia and/or private sector
  • Competent in Python programming language, tensorflow and/or pytorch
  • Strong expertise in reinforcement learning and/or deep learning

 

Desired skills and qualifications:

  • Experience with signal processing techniques
  • Familiarity with multi-threaded design and parallel/distributed computing
  • Working knowledge of source control management such as Git
  • Familiarity with AWS, GCP and Azure

 

To apply, please submit your CV and a brief cover letter to Jorin Mamen, Canadian Operations Manager (jorin@bios.health).

 

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