Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Bloomington, IN · On-site
$45.30K - $61.50K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Bloomington, IN · On-site
$45.30K - $61.50K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Support model deployment, inference services, and experiment tracking (e.g., MLflow) * Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to ...
Support model deployment, inference services, and experiment tracking (e.g., MLflow) * Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to ...
Advisor - Agent Research
Indianapolis, IN · On-site
Support model deployment, inference services, and experiment tracking (e.g., MLflow) * Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to ...
Advisor - Agent Research
Indianapolis, IN · On-site
Support model deployment, inference services, and experiment tracking (e.g., MLflow) * Integrate LLM reasoning with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to ...
Data Engineer (AI)
$135K - $155K/yr
The position requires working across departments to build, operate, and optimize highly available data pipelines that feed analytics, ML training and inference, and retrieval-augmented generation ...
Data Engineer (AI)
$135K - $155K/yr
The position requires working across departments to build, operate, and optimize highly available data pipelines that feed analytics, ML training and inference, and retrieval-augmented generation ...
Advanced computational and data-science methods, including modern ML/AI workflows for pattern recognition, clustering, anomaly detection, and inference in large and noisy datasets. Exploratory work ...
Advanced computational and data-science methods, including modern ML/AI workflows for pattern recognition, clustering, anomaly detection, and inference in large and noisy datasets. Exploratory work ...
AI and Data Science Engineer III
Indianapolis, IN · On-site +1
$109.40K - $131.40K/yr
... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... inference, and LLM applications using Claude-, GPT/Codex-, and Gemini-class models, and more ...
AI and Data Science Engineer III
Indianapolis, IN · On-site +1
$109.40K - $131.40K/yr
... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... inference, and LLM applications using Claude-, GPT/Codex-, and Gemini-class models, and more ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Strategic ML & Problem Formulation : Define ambiguous business challenges as rigorous research ... Deep understanding of experimental design, causal inference, and computational statistics.
Strategic ML & Problem Formulation : Define ambiguous business challenges as rigorous research ... Deep understanding of experimental design, causal inference, and computational statistics.
Ml Inference information
What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?
To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What are some common challenges faced by ML Inference Engineers when deploying models to production?
ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.
What is ML inference?
ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.
What is the difference between Ml Inference vs Data Scientist?
| Aspect | ML Inference | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning models, programming skills | Degree in data science, statistics, or related fields |
| Work Environment | Deploying models in production, real-time data processing | Data analysis, model development, research |
| Industry Usage | AI product deployment, software companies | Research institutions, tech firms, consulting |
ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.
What are popular job titles related to Ml Inference jobs in Indiana? For Ml Inference jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Indiana look for? The top searched job categories for Ml Inference jobs in Indiana are:
What cities in Indiana are hiring for Ml Inference jobs? Cities in Indiana with the most Ml Inference job openings:
$42.70K - $58K/yr
Full-time
Posted 8 days ago
Job description
Posting Details
Position Details
Title
Postdoctoral Fellow in Biostatistics and Health Data Science
Specific Title
Appointment Type
Postdoctoral Fellow
Department
IUSM - Biostatistics
Campus
IU School of Medicine Indianapolis
Position Summary
Postdoctoral Researcher to advance research at the intersection of artificial intelligence for healthcare, multimodal data analysis (EHRs, medical imaging, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations.
Key responsibilities include:
We have established strong collaborations with both clinical and basic science research departments and divisions and health systems including IU Health and Eskenazi Hospitals as well as strong partnerships with major research institutes and centers including Regenstrief Institute, Center for Computational Biology and Bioinformatics, Indiana Clinical and Translational Science Institute (CTSI), Indiana University Simon Comprehensive Cancer Center, Indiana Institute of Biomedical Research, and Indiana Alzheimer's Disease Research Center. Additional details about the Department and the PhD program are available on our web page: https://medicine.iu.edu/biostatistics.
The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country's second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the "best places to live" lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living. Carmel, Indy's northern neighbor, was recently named as the best mid-sized city in the country.
IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.
Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life - the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named "Best Airport in North America" by Airports Council International for many years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocationThe search will continue until the positions are filled.
Basic Qualifications
Required Qualifications:
Department Contact for Questions
Professor Jiang Bian via email at: bianj@regenstrief.org
Additional Qualifications
Special Instructions
Priority Application Review Deadline
Expected Start Date
Posting Number
Position Details
Title
Postdoctoral Fellow in Biostatistics and Health Data Science
Specific Title
Appointment Type
Postdoctoral Fellow
Department
IUSM - Biostatistics
Campus
IU School of Medicine Indianapolis
Position Summary
Postdoctoral Researcher to advance research at the intersection of artificial intelligence for healthcare, multimodal data analysis (EHRs, medical imaging, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations.
Key responsibilities include:
- Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines.
- Build reproducible research pipelines and maintain reliable experiment codebases (prefer Python).
- Apply causal inference and discovery frameworks to clinical questions.
- Translate proposed methods and frameworks into real-world clinical workflows.
- Contribute to grant proposals and research reports.
We have established strong collaborations with both clinical and basic science research departments and divisions and health systems including IU Health and Eskenazi Hospitals as well as strong partnerships with major research institutes and centers including Regenstrief Institute, Center for Computational Biology and Bioinformatics, Indiana Clinical and Translational Science Institute (CTSI), Indiana University Simon Comprehensive Cancer Center, Indiana Institute of Biomedical Research, and Indiana Alzheimer's Disease Research Center. Additional details about the Department and the PhD program are available on our web page: https://medicine.iu.edu/biostatistics.
The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country's second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the "best places to live" lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living. Carmel, Indy's northern neighbor, was recently named as the best mid-sized city in the country.
IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.
Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life - the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named "Best Airport in North America" by Airports Council International for many years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocationThe search will continue until the positions are filled.
Basic Qualifications
Required Qualifications:
- Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area.
- Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP.
- Demonstrated working experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
- Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases).
- Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.
- Experience with LLMs/foundation models (e.g., clinical NLP, retrieval-augmented generation, instruction tuning) and multimodal transformers.
- Solid understanding of causal methods (e.g., propensity scores, IPW, matching) and/or causal discovery.
- Familiarity with data engineering and MLOps (e.g., SQL, Spark, Airflow, Docker, Kubernetes).
- Knowledge of responsible/ethical AI for health: fairness/equity, interpretability, robustness, privacy (e.g., differential privacy, federated learning).
- Track record of first-author publications in relevant venues and collaborative open-source contributions.
Department Contact for Questions
Professor Jiang Bian via email at: bianj@regenstrief.org
Additional Qualifications
Special Instructions
Priority Application Review Deadline
Expected Start Date
Posting Number