WI · On-site
$68.51 - $108.47/hr
Qualifications PhD in machine learning, computer science, bioinformatics, or a related field. Strong machine‑learning modelling expertise with experience analysing large‑scale data sets.
WI · On-site
$68.51 - $108.47/hr
Qualifications PhD in machine learning, computer science, bioinformatics, or a related field. Strong machine‑learning modelling expertise with experience analysing large‑scale data sets.
As a Machine Learning Engineer II, you will be responsible for creating and validating machine learning models while collaborating with cross-functional teams to enhance power tool solutions.
As a Machine Learning Engineer II, you will be responsible for creating and validating machine learning models while collaborating with cross-functional teams to enhance power tool solutions.
The Machine Learning Engineer II will create and validate machine learning models, innovate solutions for power tools, and collaborate with cross-functional teams to enhance user experiences.
The Machine Learning Engineer II will create and validate machine learning models, innovate solutions for power tools, and collaborate with cross-functional teams to enhance user experiences.
Your role on our team As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions ...
Your role on our team As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions ...
Your role on our team As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions ...
Your role on our team As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions ...
Machine Learning Tutor
Madison, WI · Remote
$18 - $40/hr
About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...
Machine Learning Tutor
Madison, WI · Remote
$18 - $40/hr
About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...
$225K - $260K/yr
The role collaborates closely with ML researchers and infrastructure teams, influencing the design ... Hands-on experience training machine learning models across multiple GPUs or compute nodes ...
$225K - $260K/yr
The role collaborates closely with ML researchers and infrastructure teams, influencing the design ... Hands-on experience training machine learning models across multiple GPUs or compute nodes ...
Machine Learning Tutor
Milwaukee, WI · Remote
$18 - $40/hr
About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...
Machine Learning Tutor
Milwaukee, WI · Remote
$18 - $40/hr
About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...
WI · On-site
$73.95 - $108.08/hr
Als Machine Learning Engineer bouw jij de modellen die energie echt slimmer maken. Je krijgt toegang tot een schat aan real-time data en de vrijheid om modellen van experiment tot productie te ...
WI · On-site
$73.95 - $108.08/hr
Als Machine Learning Engineer bouw jij de modellen die energie echt slimmer maken. Je krijgt toegang tot een schat aan real-time data en de vrijheid om modellen van experiment tot productie te ...
WI · On-site
$105.50 - $132.20/hr
As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting‑edge machine learning models and solutions to enhance various aspects of our business operations ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we ...
Research Machine Learning Federated Learning information
What is a researcher in machine learning federated learning?
A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.
What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?
To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.
What are some common challenges faced when implementing federated learning in a research environment?
One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.
What is the difference between Research Machine Learning Federated Learning vs Data Scientist?
| Aspect | Research Machine Learning Federated Learning | Data Scientist |
|---|---|---|
| Credentials | Advanced degrees in CS, ML, or related fields; research experience | Bachelor's or Master's in Data Science, Statistics, or related fields |
| Work Environment | Research labs, academic institutions, tech companies focusing on privacy-preserving ML | Business environments, analytics teams, data-driven departments |
| Industry Usage | Developing federated algorithms, privacy-preserving ML models | Data analysis, modeling, reporting, and insights generation |
Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.
What are popular job titles related to Research Machine Learning Federated Learning jobs in Wisconsin?
For Research Machine Learning Federated Learning jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Wisconsin look for?
The top searched job categories for Research Machine Learning Federated Learning jobs in Wisconsin are:
Postdoctoral Researcher - Machine Learning
WI • On-site
EURAXESS Ireland
$68.51 - $108.47/hr
Other
Posted 9 days ago
Job description
Offer Description
#J-18808-LjbffrAntiviral drugs are used to successfully treat infections such as HIV and HCV, yet for most life‑threatening and neglected infections no such drugs exist, leaving critical gaps in epidemic and pandemic preparedness. Traditional antiviral drug discovery focuses on a small number of known targets, while the biology of viral replication is far more complex and harbors many undiscovered druggable targets. Our aim is to revolutionize antiviral target discovery by uncovering this untapped landscape. We have developed high‑throughput, multiplex, high‑content multiparametric phenotypic antiviral assays that enable the screening of hundreds of thousands of molecules in our fully automated high‑biosafety screening facility (CAPS‑IT) against multiple viruses.
ResponsibilitiesYou will design, develop, and deploy advanced machine‑learning models that exploit the full complexity of imaging data to identify promising molecules for in‑depth virological studies, ultimately creating the first‑of‑its‑kind "Atlas of Druggable Antiviral Targets". Your role will involve close collaboration with a multidisciplinary, international virology team and experts in AI and computational biology.
Key tasks include:
- Developing and optimizing ML‑driven models within our antiviral screening pipeline to unlock the richness of multidimensional data.
- Extracting and interpreting detailed phenotypic fingerprints at whole‑well and single‑cell resolution in virus‑infected cell cultures.
- Using AI models to cluster compounds, infer mechanisms of action, identify unique activity signatures, and integrate toxicity profiles to reduce false positives and guide prioritization.
- Collaborating with downstream validation groups to iteratively refine models and build an adaptive, evolving discovery pipeline.
PhD in machine learning, computer science, bioinformatics, or a related field.
Strong machine‑learning modelling expertise with experience analysing large‑scale data sets.
Experience with cellular imaging data or virology/immunology is a plus.
Core Skills- Creation and evaluation of machine‑learning models.
- Familiarity with deep‑learning frameworks such as PyTorch or TensorFlow.
- Data preparation, especially in a bioinformatics context (cleaning, filtering, etc.).
- Data fusion and advanced algorithms (deep learning, generative AI, kernel methods, Bayesian methods).
- Preferred experience with high‑content imaging or cell‑imaging data (e.g., CellProfiler, CNNs).
- Knowledge of chemo‑informatics and drug discovery.
- Strong statistical skills (batch effects, confounders, experimental design).
Fully funded position with a competitive salary at KU Leuven. Access to cutting‑edge technologies and a broad collaborative network. Initial one‑year contract, potentially extendable based on performance.