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Physics Informed Machine Learning Jobs in British Columbia

Research Associate

Vancouver, BC · On-site

CA$6.3K/mo

Integrate computational modeling and machine learning approaches to enable robust biomarker ... A Ph.D. in computer science, statistics, physics, electrical engineering, neuroscience, or a ...

Bachelor's degree in Systems Engineering or Engineering Physics. * Minimum of 5 years of experience ... Understanding of machine learning and deep learning. * Knowledge of engineering lifecycles relevant ...

... Intelligence (AI) and Machine Learning (ML) across our Development and Engineering (D&E ... Graduate degree in Computer Science or Engineering or Physics with a focus on AI, ML, or DL, or the ...

Transform complex data into actionable insights that help teams make informed business decisions ... AI, machine learning, and data-driven solutions. Qualifications * Bachelor's degree in Data ...

... machine learning pipelines, intelligent workflows, and automated decision-making. Beyond core ... data for informed engineering and product decisions. * Technical Leadership & Mentorship:

Stay informed about competitor designs. Game Improvement and Feature Analysis: * Proactively ... Utilize A/B testing and machine learning techniques. * Translate outcomes into actionable ...

Designing and deploying machine learning and predictive/generative AI models for demand forecasting ... informed by business context, stakeholder needs are met Qualifications Bachelor's degree in ...

Showing results 21-40

Physics Informed Machine Learning information

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are popular job titles related to Physics Informed Machine Learning jobs in British Columbia?

For Physics Informed Machine Learning jobs in British Columbia, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in British Columbia look for?

The top searched job categories for Physics Informed Machine Learning jobs in British Columbia are:

Infographic showing various Physics Informed Machine Learning job openings in British Columbia as of August 2026, with employment types broken down into 6% Internship, 36% Full Time, 52% Part Time, and 6% Contract. Highlights an 100% In-person job distribution.

Research Associate

Ubc

Vancouver, BC • On-site

CA$6.3K/mo

Full-time

Re-posted 2 days ago


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Job description

AcademicJob CategoryFaculty Non BargainingJob TitleResearch AssociateDepartmentMcKeown Laboratory Pacific Parkinson's Research Centre Division of Neurology | Department of Medicine | Faculty of Medicine (Martin McKeown)Posting End DateAugust 30, 2026

Note: Applications will be accepted until 11:59 PM on the Posting End Date.


Job End DateAugust 13, 2027

The expected pay for this position is $6,344/mon.

At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary conditions for a rewarding career.

JOB SUMMARY

The Pacific Parkinson's Research Centre (PPRC) is dedicated to the clinical treatment and research related to Parkinson's disease. We are seeking a Research Associate with expertise in Electroencephalogram (EEG) analysis, signal processing, and quantitative neuroscience to support ongoing research programs focused on Parkinson's disease and related movement disorders.

ORGANIZATIONAL STATUS

The Research Associate reports directly to the Principal Investigator (PI).

WORK PERFORMED

  • Developing and implementing advanced neurophysiological signalprocessing and computational models to analyze EEG data and extract clinically meaningful biomarkers related to neurological disorders.
  • Conduct research on EEG preprocessing, artifact removal, and denoising to improve data quality.
  • Develop and implement advanced algorithms for analyzing neurophysiological signals and extracting clinically meaningful biomarkers.
  • Apply advanced EEG analysis methods to study neural responses during electrical brain stimulation, including expertise in Galvanic Vestibular Stimulation (GVS) and the characterization of EEG dynamics under stimulation protocols.
  • Integrate computational modeling and machine learning approaches to enable robust biomarker discovery, validation, and evaluation of diseaserelated neural changes.
  • Apply comprehensive EEG signalprocessing frameworks to characterize neural dynamics and quantify diseaserelated alterations in patient populations.
  • Design and conduct experiments in collaboration with interdisciplinary teams; support data collection and analysis.
  • Prepare and present research findings at scientific conferences, workshops, and academic meetings.
  • Contribute to peerreviewed publications, technical reports, and grant proposals.
  • Participate in dissemination activities, including tutorials, training sessions, and collaborative workshops.
  • Stay current with advancements in EEG signal processing, computational neuroscience, and related technologies.
  • Mentor junior researchers and support lab operations as needed.
  • Perform other research-related duties as assigned

CONSEQUENCE OF ERROR/JUDGEMENT

The incumbent may have access to confidential data. The quality of work performed will determine the success of future projects and funding. Exercising poor judgement and lack of appropriate consultation with research team members and supervisors can adversely affect the viability and validity of research projects or programs, and may compromise the UBC Division's and Department's ability to secure grant-based funding for future research projects.

SUPERVISION RECEIVED

The individual in this position will report directly to the PI regarding research activities and data analysis. The incumbent will require minimal supervision and will exercise independent judgment regarding scheduling and timely completion of tasks.

SUPERVISION GIVEN

Supervises Research Assistants, Graduate and/or Undergraduate students.

QUALIFICATIONS

  • Strong educational background: A Ph.D. in computer science, statistics, physics, electrical engineering, neuroscience, or a related field, plus a minimum of 10 years of experience or the equivalent combination of education and experience.
  • Solid technical skills: Strong expertise in signal processing, quantitative analysis and machine learning.
  • Proficiency in programming languages such as Python, R, and MATLAB is essential. Experience working with big databases and have completed requirements to all PHSA pre-requisitions for working with human data.
  • Experience in healthrelated research or clinical neuroscience is an asset.
  • Strong problemsolving skills: Ability to identify critical scientific questions in the health sector and apply appropriate analytical methods to address them.
  • Strong communication and collaboration skills: Must be able to communicate effectively with researchers, clinicians, and interdisciplinary teams.
  • Leadership skills: Experience mentoring junior researchers and contributing to teambased scientific environments. Must display advanced organizational and administrative skills.
  • Must have experience with / or been responsible for data analyses, writing of manuscripts, preparation of figures, and publication of data, as demonstrated in candidates' publications.
  • Must have good interpersonal skills, be motivated, adaptable to a changing work environment and be energetic with a solid work ethic.
  • Project management: Ability to plan, organize, and manage multiple research projects effectively.
  • Flexibility and adaptability: Ability to adopt new research methods, technologies, and analytical approaches as needed.
  • Continuous learning: Must be inquisitive and eager to learn new skills and knowledge to stay current in their field and improve their research skills.

The expected pay for this full-time position is $76,128 per year.

UBC - One of the World's Leading Universities.

As one of the world's leading universities, the University of British Columbia creates an exceptional learning environment that fosters global citizenship, advances a civil and sustainable society, and supports outstanding research to serve the people of British Columbia, Canada and the world.

UBC hires on the basis of merit and is committed to employment equity. All qualified persons are encouraged to apply. Equity and diversity are essential to academic excellence. An open and diverse community fosters the inclusion of voices that have been underrepresented or discouraged. We encourage applications from members of groups that have been marginalized on any grounds enumerated under the B.C. Human Rights Code, including sex, sexual orientation, gender identity or expression, racialization, disability, political belief, religion, marital or family status, age, and/or status as a First Nation, Metis, Inuit, or Indigenous person. All qualified candidates are encouraged to apply; however, Canadians and permanent residents of Canada will be given priority.


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