# Scientific Support

<div id="bkmrk-"></div>#### Need help with Machine Learning in your research?

Contact us at <helpdesk@unil.ch> with subject: DCSR ML support

Scientific support for Machine Learning projects, as outlined below, is provided free of charge to all UNIL members.

#### Introduction

Machine Learning provides a powerful framework for predictive modeling in scientific research:

- Infer outcomes from complex datasets using classification and regression models
- Evaluate and improve models based on predictive performance
- Use exploratory techniques to better understand and prepare your data

<div id="bkmrk--5"></div>At DCSR, we support researchers in several key areas of Machine Learning:

#### Training

We help you understand how specific Machine Learning methods work and how to apply them in your research. We also offers short introductory courses on Machine Learning; see [ML courses](https://wiki.unil.ch/ci/books/research-support/page/courses).

#### Methodology

We assist you in selecting and applying appropriate Machine Learning methods for your research.

This may include:

- A pilot phase, where we collaboratively develop and test code on your laptop or UNIL clusters
- A production phase, where we help scale and refine your workflow

More specifically, we can:

- Identify existing tools suited to your analysis
- Help install and run them on your laptop or UNIL clusters
- Explain key parameters and settings
- Help develop custom algorithms and code if no suitable tools exist

#### Infrastructure

We help you efficiently run your Machine Learning workflows on UNIL clusters.

This includes:

- Installing and configuring your code
- Profiling performance to optimize resource usage (RAM, CPUs/GPUs, number of nodes)

#### Collaboration at UNIL

We can connect you with relevant experts at UNIL to discuss specific Machine Learning challenges.

#### Example Use Cases:

1. Experimental scientist  
    Wants to analyze data using Machine Learning on a laptop or UNIL clusters.  
    → We help identify suitable tools, explain how they work, and support their use.
2. Data scientist (setup phase)  
    Wants to implement a Machine Learning pipeline but is unsure how to proceed.  
    → We help select and apply appropriate methods.
3. Data scientist (review phase)  
    Has implemented a pipeline and wants feedback.  
    → We review the methodology and suggest improvements or alternatives.
4. Scaling from laptop to cluster  
    Wants to move a pipeline from a local computer to UNIL clusters.  
    → We assist with deployment, software setup, and performance optimization.

#### Contact

You can reach us at <helpdesk@unil.ch> with subject: DCSR ML support