Postdoctoral Research Fellow

Icahn School of Medicine at Mount Sinai — Department of Artificial Intelligence and Human Health
Location: New York, NY

The Liu Lab in the Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai is recruiting a Postdoctoral Research Fellow. Our lab develops interpretable artificial intelligence frameworks that integrate digital health data (e.g., wearable sensor time-series), genomics, and electronic health records to advance discovery in complex neurological and psychiatric disorders, including depression, ADHD, Parkinson’s disease, and Alzheimer’s disease.

The postdoc will contribute to projects involving large-scale digital phenotyping, multimodal data integration, advanced time-series modeling, and AI-driven identification of biological and clinical markers of disease risk, progression, and treatment response.

Interested applicants may email their CV to contact@liujlab.org.

Responsibilities

• Develop and implement AI/ML models for high-dimensional time-series, genomic, and clinical data
• Perform data preprocessing, analysis, and multimodal integration
• Develop interpretable and explainable ML methods for biomedical applications
• Build computational pipelines, reproducible workflows, and internal tools
• Prepare manuscripts, figures, and visualizations for publications and presentations
• Collaborate with faculty, clinicians, and researchers across Mount Sinai
• Participate in lab meetings, seminars, workshops, and collaborative projects

Required Qualifications

• PhD in Computer Science, Computational Biology, Bioinformatics, Statistics, Biomedical Data Science, Neuroscience, or a related quantitative field
• Strong programming skills (e.g., Python, R)
• Background in machine learning, deep learning, statistical modeling, or related areas
• Experience working with large datasets and computational tools
• Strong written and oral communication skills

Preferred Experience

• Demonstrated experience in AI/ML, data science, or computational biology
• Experience with time-series modeling, genomics, digital health, or multimodal data (preferred but not required)

Location CAN, ON, Toronto

Description Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer.

As a Principal Applied Scientist, you will work with talented peers pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors.

This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work at scale. This position requires experience with developing Computer Vision, Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms.

Johns Hopkins University

We invite applications for Postdoctoral Fellow positions in the broad areas of data science and AI, with a focus on developing and applying novel data science approaches, computational tools and statistical methods to advance health and biomedical research. Johns Hopkins University has recently made transformative new investment in launching a new Data Science and AI institute that will serve as the hub for interdisciplinary data collaborations with faculties and students from across Johns Hopkins and will build the nation’s foremost destination for emerging applications, opportunities and challenges presented by data science, machine learning and AI.

San Jose, CA, USA

We are looking for a hands-on, systems-oriented AI Agent Engineer to design, build, and maintain intelligent agents that drive automation and business impact across the enterprise. This role is responsible for the full lifecycle of agent development — from design to versioning, orchestration, and continuous learning.

You’ll contribute directly to scaling our AI strategy by engineering reusable components, optimizing agent workflows, and ensuring real-world performance in production environments.

What you'll Do

• Agent Development: Build and fine-tune specialized AI agents for targeted customer experience use cases such as discovery, support, and lead qualification. Implement prompt engineering strategies, memory handling, resource management and tool-calling integrations

• Multi-Agent Communication: Adopt agent-to-agent communication protocols and handoff mechanisms to enable cooperative task execution and delegation. Build orchestrated workflows across agents using frameworks like LangChain, AutoGen, or Semantic Kernel

• Templates & Reusability: Create reusable agent templates and modular components to accelerate deployment across business units. Build plug-and-play configurations for domain-specific requirements.

• Lifecycle Management & Monitoring: Track and improve conversation quality, task success rate, user satisfaction, and performance metrics. Automate monitoring of agent behavior using observability tools (e.g., Arize, LangSmith, custom dashboards)

• Continuous Improvement: Implement learning workflows, including human-in-the-loop feedback and automatic retraining. Refine prompts and model behavior through structured experimentation and feedback loops.

• Maintenance & Governance: Handle knowledge base updates, drift detection, performance degradation, and integration of new business logic. Ensure agents stay aligned with evolving enterprise data sources and compliance requirements

• Deployment: Manage agent versioning, testing pipelines (unit, regression, UX), and controlled rollout processes. Collaborate with DevOps, QA, and infrastructure teams to ensure scalable deployments

What you need to succeed - 3–5+ years of experience in AI/ML engineering, NLP systems, or backend development - Strong proficiency with LLM frameworks (e.g., OpenAI APIs, LangChain, RAG pipelines) - Experience building conversational agents or workflow bots in production environments - Familiarity with cloud platforms (AWS/GCP/Azure), REST APIs, Python, and containerization (Docker, K8s) - Comfort with prompt design, vector databases, and memory handling strategies

Preferred Qualifications - Experience with multi-agent frameworks or agent orchestration systems - Familiarity with observability tools, data labeling workflows, or synthetic data generation - Background in conversational design or dialogue management systems - Degree in Computer Science, Data Science, Engineering, or a related field

Remote US or Canada

Mission: Join the team that builds and operates Groq’s real-time, distributed inference system delivering large-scale inference for LLMs and next-gen AI applications at ultra-low latency. As a Low-Level Production Engineer, your mission is to ensure reliability, fault tolerance, and operational excellence in Groq’s LPU-powered infrastructure. You’ll work deep in the stack—bridging distributed runtime systems with the hardware—to keep Groq systems fast, stable, and production-ready at scale.

Responsibilities & opportunities in this role: Production Reliability: Operate and harden Groq’s distributed runtime across thousands of LPUs, ensuring uptime and resilience under dynamic global workloads. Low-Level Debugging: Diagnose and resolve hardware-software integration issues in live environments, from datacenter level events to single component failures. Observability & Diagnostics: Build tools and infrastructure to improve real-time system monitoring, fault detection, and SLO tracking. Automation & Scale: Automate deployment workflows, failover systems, and operational playbooks to reduce overhead and accelerate reliability improvements. Performance & Optimization: Profile and tune production systems for throughput, latency, and determinism—every cycle counts. Cross-Functional Collaboration: Partner with compiler, hardware, infra, and data center teams to deliver robust, fault-tolerant production systems.

Ideal candidates have/are: Proven experience in production engineering across the stack and operating large-scale distributed systems. Deep knowledge of computer architecture, operating systems, and hardware-software interfaces. Skilled in low-level systems programming (C/C++ or Rust), with scripting fluency (Python, Bash, or Go). Comfortable debugging complex issues close to the metal—kernels, firmware, or hardware-aware code paths. Strong background in automation, CI/CD, and building reliable systems that scale. Thrive across environments—from kernel internals to distributed runtimes to data center operations. Communicate clearly, make pragmatic decisions, and take ownership of long-term outcomes.

Nice to have: Experience operating high-performance, real-time systems at scale (ML inference, HPC, or similar). Familiarity with GPUs, FPGAs, or ASICs in production environments. Prior exposure to ML frameworks (e.g., PyTorch) or compiler tooling (e.g., MLIR). Track record of delivering complex production systems in high-impact environments.

Attributes of a Groqster: Humility – Egos are checked at the door Collaborative & Team Savvy – We make up the smartest person in the room, together Growth & Giver Mindset – Learn it all versus know it all, we share knowledge generously Curious & Innovative – Take a creative approach to projects, problems, and design Passion, Grit, & Boldness – No-limit thinking, fueling informed risk taking

Preference for on-site candidates in San Mateo, but remote possible.

BigHat is hiring a Principal ML Scientist. We've got an awesome high-throughput wetlab that pumps proprietary data into custom ETL and ML Ops infra to power our weekly design-build-train loop. Come solve hard-enough-to-be-fun problems in protein engineering in service of helping patients!

Successful candidates will contribute to building and deploying AI-powered systems, including automated code generation, smart agents, retrieval-augmented generation (RAG) frameworks, and tools that integrate cutting-edge AI with scientific software and machine learning research. These systems aim to support drug discovery programs, increase research productivity, and improve the quality and efficiency of ML model training through intelligent data workflows and feedback loops. Candidates should have a strong interest in artificial intelligence (specifically, generative and agentic AI), with responsibilities spanning end-to-end system design: from idea conception and rapid prototyping to production-scale deployment. They should be comfortable working in a fast-paced environment where innovation, experimentation, and rigorous software engineering are all valued, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of AI. For more information, visit www.DEShawResearch.com.

Please apply using this link:

https://apply.deshawresearch.com/careers/Register?pipelineId=923&source=NeurIPS_1

The expected annual base salary for this position is USD 250,000 – USD 600,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.

Open positions in Mathematical, Statistical and Computational aspects of Artificial Intelligence and Machine Learning.

The Faculty of Mathematics and the Institute for Mathematical and Computational Engineering (IMC) from the Pontificia Universidad Católica de Chile (UC) are offering up to two full-time professor positions at the assistant or associate level. The successful candidate will have a joint appointment between the Faculty of Mathematics and the IMC. We welcome applications from highly qualified candidates with a track record of excellent research and a solid commitment to excellence in teaching and supervision. This position is intended for those researchers interested in advancing the mathematical, statistical and computational aspects of modern artificial intelligence, machine learning and its applications. Candidates conducting research in areas including pure and computational mathematics, mathematical modeling, statistics, optimization, and theoretical computer science, that exhibit a convincing path towards AI research are also encouraged to apply.

The successful candidate will contribute to teaching courses at the Institute for Mathematical and Computational Engineering and the Faculty of Mathematics. The typical teaching load is three semester-long courses a year, at the graduate or undergraduate level, split between the two academic units. Command of Spanish is not required for applying; however, the selected candidate is expected to start teaching in Spanish in at most two years. Applicants who do not speak Spanish will receive support from the University to learn the language adequately. To support the initial academic career stages, the incorporation of Assistant Professors can benefit from an initial reduction of the teaching load and a starting fund. The applicant position and salary will be adjusted, according to the policies and regulations at UC, on the applicant starting date. These will be determined according to the previous experience of the candidate. UC is committed to the values of diversity regarding origin, gender, and ethnicity to build a more diverse and inclusive community. For the current call, women are especially encouraged to apply.

APPLICATIONS

Applications are received through MATHJOBS platform at the link https://www.mathjobs.org/jobs/list/27675 or can be sent by email at vacancysearch.imc@uc.cl. The documentation submitted must include at least:

• Cover letter.
• Curriculum vitae.
• Publication list.
• Research statement.
• Teaching statement.
• Three (3) letters of recommendation, with ideally at least one of them referring to the teaching experience.

The deadline for the applications is December 16th, 2025. Queries can be made by email to Prof. Cristóbal Rojas at vacancysearch.imc@uc.cl.

VISA

If selected for a position, a foreigner without permanent residency in Chile or applying from abroad will need to request a visa in the consulate of their country of origin to be incorporated as academic staff at UC Chile.

Further information about IMC and the Faculty of Mathematics can be found at https://imc.uc.cl and www.mat.uc.cl.

We are Bagel Labs - a distributed machine learning research lab working toward open-source superintelligence.

Role Overview

We encourage curiosity-driven research and welcome bold, untested concepts.
You will push the boundaries of diffusion models and distributed learning systems, testing hypotheses at the intersection of generative AI and scalable infrastructure.
We love novel, provocative, and untested ideas that challenge conventional paradigms.

Key Responsibilities

• Prototype AI methodologies that can redefine distributed machine learning.
• Pioneer next-generation diffusion architectures including rectified flows, EDM variants, and latent consistency models that scale across distributed infrastructures.
• Develop novel sampling algorithms, guidance mechanisms, and conditioning strategies that unlock new capabilities in controllable generation.
• Partner with cryptographers and economists to embed secure, incentive-aligned protocols into model pipelines.
• Publish papers at top-tier ML venues, organize workshops, and align our roadmap with the latest academic advances.
• Share insights through internal notes, external blog posts, and conference-grade write-ups (for example, blog.bagel.com).
• Contribute to open-source code and stay active in the ML community.

Who You Might Be

You are extremely curious and motivated by discovery.
You actively consume the latest ML research - scanning arXiv, attending conferences, dissecting new open-source releases, and integrating breakthroughs into your own experimentation.
You thrive on first-principles reasoning, see potential in unexplored ideas, and view learning as a perpetual process.

Desired Skills (Flexible)

1. Deep expertise in modern diffusion models, score matching, flow matching, consistency training, and distillation techniques.
2. Hands-on experience with distributed training frameworks such as FairScale, DeepSpeed, Megatron-LM, or custom tensor and pipeline parallelism implementations.
3. Strong mathematical foundation in SDEs, ODEs, optimal transport, and variational inference for designing novel generative objectives.
4. Clear and concise communication skills.
5. Bonus: experience with model quantization (QLoRA, GPTQ), knowledge distillation for diffusion models, or cryptographic techniques for secure distributed training.

What We Offer

• Top-of-market compensation and time to pursue open-ended research
• A deeply technical culture where bold ideas are debated, stress-tested, and built
• Remote flexibility within North American time zones
• Ownership of work shaping decentralized AI
• Paid travel to leading ML conferences worldwide

Apply now - help us build the infrastructure for open-source superintelligence.

Amsterdam

As a Quantitative Research Intern, you will get to work with our research team of mathematicians, scientists and technologists, to help develop the models that drive Optiver’s trading. You will tackle a practical research challenge that has impact and directly influences Optiver’s trading decisions. In our business, where the markets are always evolving, you will use your skills to predict its movements.

What you’ll do Led by our in-house education team, you will delve into trading fundamentals and engage in research projects that make a real difference. You will enhance your understanding of trading principles and gain hands-on experience by trading on live markets using real Optiver technology, with simulated capital. For the ten-week internship, you will get support from experienced researchers during your research project work, providing you exposure to a variety of areas, including: • Deep dive into trading and research fundamentals, from theoretical concepts to financial markets, strategies and cutting-edge technology • Using statistical models and machine learning to develop trading algorithms • Leveraging big data technologies to analyse trading strategies and financial instruments to identify trading opportunities • Combining quantitative analysis and high-performance implementation to ensure efficiency and accuracy of your models • Gain exposure to various trading and research desks and experience the financial markets first-hand Based on your performance during the internship, you could receive an offer to join our firm full-time after your studies.

What you’ll get You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talented colleagues, working collectively to tackle the toughest problems in the financial markets. In addition, you’ll receive: • A highly competitive internship compensation package • Optiver-covered flights and accommodation in the city centre for the duration of the internship • Extensive office perks, including breakfast and lunch, world-class barista coffee and Friday afternoon drinks • The opportunity to participate in sports and leisure activities, along with social events exclusively organised for your intern cohort

Who you are • Penultimate year student in Mathematics, Statistics, Computer Science, Physics or a related STEM field, with the ability to work full time upon graduation in 2027 • Solid foundation in mathematics, probability and statistics • Excellent research, analytical and modelling skills • Independent research experience • Proficiency in any programming language • Knowledge of machine learning, time-series analysis and pattern recognition is a plus • Strong interest in working in a fast-paced, collaborative environment • Fluent in English with strong written and verbal communication skills

Diversity statement Optiver is committed to diversity and inclusion. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities. Please let us know if you would like to request any reasonable adjustments by contacting the Recruitment team via the contact form, selecting “Reasonable Adjustments” as the subject of your inquiry.

For answers to some of our most frequently asked questions, refer to our Campus FAQs.

For applicants based in India, our entry route is via the placement office internship hiring season (July/August).

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2026.