Research Intern - Medical Image Reconstruction jobs at Microsoft in Taiwan, Taoyuan City

As a Software Engineering PhD Intern, you will work with teammates to solve problems and build innovative software solutions. You will apply your passion for customers and product quality as you provide technical guidance to Technical Program Managers and Product Managers. You will learn and adopt relevant new technologies, tools, methods, and processes to leverage in your solutions. This opportunity will enable you to advance your career by designing, developing, and testing next-generation software that will empower every person and organization on the planet to achieve more.

Required Qualifications

• Pursuing a PhD in Computer Science, Engineering, or related field during the academic term immediately before the internship.

• Must have at least 1 semester/term remaining following the completion of the internship.

• At least one year of programming experience in an object-oriented language.

Preferred Qualifications

• Ability to demonstrate an understanding of computer science fundamentals, including data structures and algorithms.
• Academic/research experience with distributed systems, network architecture, operating systems, and/or related topics.

The base pay range for this internship is USD $5610.00 - $11010.00 per month. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $7270.00 - $12030.00 per month.

• Applies engineering principles to solve complex problems through sound and creative engineering.

• Works with appropriate stakeholders to determine user requirements for a feature.

• Quickly learns new engineering methods and incorporates them into work processes.

• Seeks feedback and applies internal or industry best practices to improve technical solutions.

• Demonstrates skill in time management and completing software projects in a cooperative team environment.

• Reviews current developments and proactively seeks new knowledge that will improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale.

is committed to advancing cutting-edge AI technologies that enable deeper understanding and interaction with people, objects, and environments in the 3D real world. Our research spans a diverse range of areas, including computer vision, generative AI, 3D perception, and robotic action learning, among others relevant to Embodied AI. By pushing the boundaries of these domains, we aim to develop innovative solutions that bridge the gap between the digital and physical worlds, empowering AI systems to perceive, comprehend, and navigate complex real-world scenarios.

Required Qualifications:

• Currently enrolled in a PhD program in Robotics, Machine Learning, Computer Science, or a related STEM field.
• Research experience inembodied AI, robotics, AI models, natural language, computer vision, demonstrated for example through research in a related PhD program and/or publications in conferences or scientific journals.
• Hands-on experience with Python and modern deep learning frameworks.
• Excellent problem-solving skills and the ability to work independently as part of a team.
• Strong communication skills and the ability to present complex ideas clearly.

Other Requirements:

• Ability to physically work from Microsoft Research Asia – Tokyo (Japan) for the duration of the internship.
• Must obtain permission from your academic advisor and commit to at least four months of internship.

Preferred/Additional Qualifications:

• Proven software engineering skills, evidenced by professional experience, internships, and impactful open-source contributions.
• Practical experience with handling data and robot learning, such as experience in Vision-Language-Action modelsor Hand Object Interactions.
• Familiarity with1)robot learning for robot hand manipulationor 2) hand pose estimation techniques or 3) reasoning techniques (Chain-of-Thought) used in LLM.

• Contribute to a high-impact research agenda within the context of a highly collaborative research culture alongside a team of experts in Embodied AI.
• Design and implement experiments to test new hypotheses and validate research findings.
• Communicate research findings to an interdisciplinary research team.
• Prepare technical papers, presentations, and open-source releases of research code.

At , we believe machine learning and artificial intelligence has the potential to transform scientific modelling and discovery crucial for solving the most pressing problems facing society including sustainable materials and discovery of new drugs.

Required:

• PhD or equivalent experience in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field.
• Strong software engineering in Python (packaging, testing, CI), with systems thinking for data‑intensive ML.
• Deep learning experience (PyTorch/JAX/TensorFlow) and solid foundations in linear algebra, probability, and statistics.
• Proven experience designing robust data pipelines for large‑scale ML (HPC or cloud).
• Ability to reason about learning signal and to assess information content of real‑world scientific datasets.
• Excellent collaboration and communication in interdisciplinary teams.

Preferred:

• Hands‑on cryo‑EM experience (e.g., map reconstruction, refinement, or pipeline tooling).
• CUDA or C++ for performance‑critical components; experience with mixed precision and memory‑efficient training.
• Experience integrating experimental data into ML models (e.g., constraints/priors from cryo‑EM, binding assays, spectroscopy).
• Familiarity with MD data, structure prediction systems, or protein design work-flows.
• Experience with cost‑optimization for data collection and cloud utilization; clear track record of building reliable, maintainable research software at scale.
• Experience with structural biology or molecular biology data/techniques (e.g., cryo‑EM, binding assays, spectroscopy, expression, sequencing)

• Data integration for structure & dynamics: Build ingestion/curation pipelines for structural/biophysical data (mmCIF/PDB, EM maps/particles, binding/biophysics, spectroscopy); implement map/volume preprocessing (e.g., resolution filtering, normalization) and alignment to model inputs/outputs.
• Cryo‑EM expertise: Operationalize end‑to‑end flows from raw image stacks/particles to 3D maps and model‑ready tensors; interoperate with community formats (e.g., EMDB/EMPIAR, mmCIF) and link to sequences/annotations.
• Signal & information content: Design dataset diagnostics (e.g., mutual‑information‑like measures, effective sample size, SNR proxies) to quantify what data teach the model; build active‑learning loops that maximize learning per euro of data collection time.
• Model‑aware data services: Implement scalable, versioned data services and feature stores that feed training/evaluation; design loaders/augmentations optimized for throughput and correctness (GPU‑aware).
• Training‑at‑scale engineering: Own distributed data pipelines and orchestration for large runs on Azure; profile and tune I/O, storage tiers, data locality, and caching; monitor cost, utilization, and failure modes.
• Quality, governance, and reproducibility: Codify schemas/ontologies, metadata contracts, unit/integration tests, and lineage; automate validation and data drift detection; maintain documentation and examples.
• Partner across disciplines: Work closely with ML researchers, structural biologists, and drug designers; translate experimental constraints into robust computational workflows; communicate clearly and proactively.

You will be responsible for the design, development, and execution of an exciting research agenda in collaboration with other machine learning, engineers, clinicians, social scientists, and designers at Health Futures. To learn more about this opportunity, please visit:

Qualifications

• PhD in Machine Learning, Computer Science or related fields or equivalent experience.

Required

• Relevant years of experience working on a multidisciplinary team working on AI research for real world impact.
  
• Hands-on experience with large scale deep learning models and libraries (e.g., PyTorch, TensorFlow).
  
• Strong software development skills.

Preferred

• Publications at top conferences and journals such as: NeurIPS, ICML, ICLR, MICCAI, Nature, CVPR, ICCV, EMNLP, ACL.
  
• Experience with medical domains such as radiology, digital pathology, genetics, immunology.
  
• Machine learning expertise in multi-modal learning, large language models (e.g., alignment), reinforcement learning and/or domain adaptation and data-efficient learning.

The ideal candidate will have a strong intellectual curiosity and passion to solve real-world problems in healthcare and multi-modal AI The responsibilities will include:

• Advance multi-modal medical AI, empowering internal and external partners to build and deploy state of the art medical imaging AI to make clinical work-flows faster and safer and improve patient outcomes.
• Collaborate on design, implementation and evaluation of multi-modal machine learning solutions which consider key clinical factors and responsible AI.
• Support the strategic planning of the team by providing engineering and research leadership.
• Engage with external and internal collaborators to drive real world impact.

• Currently enrolled in a master or PhD program in Computer Science, Electrical Engineering, Mathematics or a related field.

• Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.In addition to the qualifications above, you’ll need to submit a minimum of two reference letters for this position. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
• In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.

• Ability to work independently and collaboratively in a dynamic and vibrant research environment.
• Willingness to embrace knowledge/technique outside your field of research.
• Solid programming skill in Python, PyTorch and experience conducting experiments and writing papers.
• Experience in LLM pre-training, post-training and inference.
• Reinforcement Learning experiences and frameworks (like VeRL, rLLM)
• Relevant publication such as CVPR, ACL, ICML, ICCV, ECCV, NeurIPS, ICLR, EMNLP etc.

Intern - MSR- The typical base pay range for this role across Canada is CAD $5,600 - CAD $10,000 per month.

Find additional pay information here:

• Research and Analysis: Conduct thorough research on emerging trends in LLM, prompt engineering, multimodal foundation model.
• Documentation and Reporting: Prepare detailed documentation of simulations, methodologies, and findings. Present results and insights to team members and stakeholders.
• Innovation and Problem-Solving: Identify challenges and bottlenecks in AI infrastructure and propose innovative solutions.

• Currently enrolled or accepted in a PhD program in Computer Science, Software Engineering, Electrical Engineering, or a related STEM field.
• At least 1 year of experience in conducting research and authoring peer-reviewed publications.

Other Requirements

• Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
• In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.

• Experience in machine learning, natural language processing, or related AI areas (e.g., LLMs, AI agents, Reasoning).
• Hands-on experience with one or more topics: large language model fine-tuning, reinforcement learning, prompt engineering, agent memory architectures, planning and reasoning modules, or multi-agent system design and implementation.
• Strong written and verbal communication skills.
• Ability to collaborate effectively in cross-functional, multi-disciplinary teams across research and product groups.
• Research contributions demonstrated through publications in top venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, IJCAI, KDD or related conferences.

Required Qualifications:

• Bachelor's Degree in Industrial Design, Product Design, Human Computer Interaction, User Experience, Interaction Design, or related field AND 5+ years experience working in product or service design
  • OR Master's Degree in Industrial Design, Product Design, Human Computer Interaction, User Experience, Interaction Design, or related field AND 4+ years experience working in product or service design
  • OR equivalent experience (e.g., demonstrated experience working in product or service design or using design thinking to solve problems).
• 3+ years of experience in research operations, user research, or related operational roles.
• Proven track record of building and scaling operational systems and processes.
• Solid project management skills with ability to manage multiple initiatives simultaneously.
• Experience with research tools and platforms (e.g., UserTesting, Qualtrics, participant recruitment tools, etc.)
• Good understanding of research methodologies and the research lifecycle.
• Experience leveraging data to inform decisions and drive change.

• Scale research programs and maximize impact across the organization.
• Design and launch programs that reduce process friction and enhance researcher impact and satisfaction.
• Design and manage operational systems for Experience Reviews, Experience Audits, and Customer Programs (Orion).
• Create scheduling protocols and operational procedures to ensure research activities run smoothly and efficiently.
• Track research deliverables and team capacity to optimize resource allocation and workload management.
• Establish metrics and reporting mechanisms to demonstrate research program value and impact.
• Build and maintain research governance frameworks, including standard operating procedures around legal, procurement, and data privacy considerations.
• Establish project operations management processes and guidelines to ensure consistency and compliance.
• Partner with legal, procurement, and security teams to create streamlined approval processes for research activities.
• Develop and maintain documentation of research standards and best practices.
• Manage end-to-end relationships with research vendors, including capability assessment, pricing negotiation, and quality oversight.
• Oversee project management and incentive programs for research participants.
• Source, evaluate, and onboard suppliers who can support various research methodologies including benchmarks and specialized research techniques.
• Negotiate contracts and manage vendor performance to ensure quality and cost-effectiveness.
• Facilitate comprehensive onboarding for new hires and contractors, including research tool access, knowledge base orientation, and process training.
• Manage offboarding processes to ensure smooth transitions and knowledge preservation.
• Manage the research technology stack, including licensing, access provisioning, and usage reporting.
• Oversee budget allocation and ensure compliance with best practices across research tools.
• Maintain and optimize tools including UserTesting, dscout, Qualtrics, Respondent.io, Tango, Marvin, and other platforms.
• Evaluate new tools and make recommendations for technology investments that enhance research capabilities.
• Identify opportunities to leverage automation and AI to improve research efficiency and impact.
• Create and maintain systems for organizing, storing, and sharing research insights to maximize accessibility and reuse.
• Develop and manage a centralized library of research templates, guides, and best practices.
• Implement processes that make research findings easily discoverable by stakeholders across the business.
• Establish tagging, categorization, and search functionality to support knowledge retrieval.
• Scale research impact through enablement programs that empower teams to access and apply research insight.
• Ensure research insights reach key stakeholders through multiple channels including monthly newsletters, SharePoint sites, and Teams channels.
• Develop both internal team communications and external-facing research communications strategies.
• Manage and evolve the team's visual identity and brand presence.
• Create communication templates and guidelines to maintain consistency in how research is shared.