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Internship Machine Learning Neuroscience Jobs in Alberta

Exposure to real-time computing, big data technologies, or machine learning through coursework, internships, or project experience is a plus. Benefits 1. Health Insurance, PTO, stock option 2. The ...

Leverage natural language processing (NLP), LLM, and machine learning (ML) techniques, including ... and internship opportunities. * Global Impact: collaborate on impactful projects for top global ...

Internship Machine Learning Neuroscience information

What are the key skills and qualifications needed to thrive as an Internship Machine Learning Neuroscience, and why are they important?

To thrive in an Internship Machine Learning Neuroscience role, you generally need a background in neuroscience, computer science, or a related field, along with a solid understanding of machine learning concepts. Experience with programming languages such as Python, libraries like TensorFlow or PyTorch, and familiarity with neuroimaging software are commonly required. Strong analytical thinking, problem-solving skills, and effective communication help you work collaboratively and adapt to complex research environments. These skills are essential for contributing meaningfully to interdisciplinary projects at the intersection of neuroscience and artificial intelligence.

What is the difference between Internship Machine Learning Neuroscience vs Internship Data Science?

AspectInternship Machine Learning NeuroscienceInternship Data Science
Required CredentialsBackground in neuroscience, machine learning, programmingBackground in statistics, programming, data analysis
Work EnvironmentResearch labs, healthcare, academia, tech companiesBusiness, tech firms, research institutions
Industry UsageNeuroscience research, AI development, healthcare techBusiness analytics, product development, consulting

Internship Machine Learning Neuroscience focuses on applying machine learning techniques to neuroscience data, often within research or healthcare settings. In contrast, Internship Data Science covers a broader range of data analysis across industries. Both roles require programming skills, but the focus and industry applications differ significantly.

What is an Internship in Machine Learning Neuroscience?

An Internship in Machine Learning Neuroscience is a temporary position, often for students or recent graduates, that involves applying machine learning techniques to neuroscience research. Interns may work on projects such as analyzing brain imaging data, modeling neural networks, or developing algorithms to understand brain function. These internships provide hands-on experience in both computational methods and neuroscience concepts, helping interns build valuable skills for future academic or industry roles. Opportunities can be found in universities, research institutes, or technology companies with neuroscience divisions.

What types of projects do interns typically work on in a Machine Learning Neuroscience internship?

Interns in Machine Learning Neuroscience often engage in projects that combine data analysis, algorithm development, and neuroscience research. This can include tasks such as preprocessing neural data, building and evaluating machine learning models to interpret brain signals, or developing tools for data visualization. Interns frequently collaborate with both data scientists and neuroscientists, gaining hands-on experience with real-world datasets and exposure to interdisciplinary research environments. These projects help interns build practical skills and contribute meaningful insights to ongoing research.
What cities in Alberta are hiring for Internship Machine Learning Neuroscience jobs? Cities in Alberta with the most Internship Machine Learning Neuroscience job openings:
Delivery Engineer - Canada

Delivery Engineer - Canada

DataVisor

Calgary, AB

CA$80K - CA$120K/yr

Full-time

Medical, PTO

Posted 3 days ago


Job description

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely and enables organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine and investigation tools work together to provide guaranteed performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview:

We are seeking a Delivery Engineer to join our Delivery team. The ideal candidate will lead client communications and drive end-to-end project delivery, while designing and implementing innovative integration solutions that meet both technical and business requirements.This role requires close collaboration with clients, sales teams, and internal stakeholders to ensure successful project outcomes. As a technical delivery leader, the TPM will own and manage all engineering work streams, coordinating across teams to keep projects on schedule and aligned with scope. The role is hands-on, requiring the ability to contribute directly to technical implementation and problem-solving as needed.

Key Responsibilities:

  • Manage and drive client communications throughout the entire project delivery, align different client stakeholders, ensure project is delivered successfully without delay.
  • Work closely with clients, sales teams, and other stakeholders to ensure successful project outcomes.
  • Understand the client's product and business logic to recommend the best solution that addresses specific client needs. Provide strong solution and consulting services.
  • Take the project management role to ensure the client onboarding project runs as expected and under control.
  • Provide technical expertise and guidance throughout the implementation and deployment phases, to integrate with the client systems.
  • Work closely with product and engineering teams to ensure seamless integration of new features and technologies into existing systems.
  • Stay updated on financial services industry trends and emerging technologies to continuously improve our solutions and offerings.
  • Conduct presentations and demonstrations of proposed solutions to clients and stakeholders.

Requirements

Qualifications:

  • Bachelor's degree in Computer Science, Engineering or a related field is required; a Master's degree in a business-related discipline (e.g., MBA, MIS, Information Systems, Management, or Analytics) is preferred.
  • Basic understanding of software development, system architecture, and system integration concepts; exposure to SaaS platform data integration is a plus.
  • Strong verbal and written communication skills, with the ability to clearly explain technical concepts to both technical and non-technical audiences.
  • Enjoys working with clients and stakeholders, with a service-oriented mindset and interest in client-facing responsibilities.
  • Ability to work collaboratively in a team environment and support multiple projects under guidance and supervision.
  • Demonstrates strong problem-solving skills, attention to detail, and a willingness to learn in a fast-paced environment.
  • Familiarity with anti-fraud concepts in the financial services or internet industry is a plus.
  • Exposure to real-time computing, big data technologies, or machine learning through coursework, internships, or project experience is a plus.

Benefits

1. Health Insurance, PTO, stock option

2. The expected salary range for this role is CAD $80,000 - $120,000 per year, depending on experience, qualifications, and location. Final compensation will be determined based on job-related skills and business needs