Alberto B.

Key Skills

Software

Other

Top Subject Matter

Neuroscience image segmentation and graph-based image analysis

Spatial single-cell metabolomics

mass spectrometry

Top Data Types

Image

Computer Code Programming

Top Task Types

Segmentation

Bounding Box

Fine-tuning

Object Detection

Classification

Freelancer Overview

Machine Learning Engineer (Internship & PhD Research) — HCI–University of Heidelberg. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Doctor of Philosophy, Link University of Heidelberg (2021) and Master of Science in Physics, University of Heidelberg (2016). AI-training focus includes data types such as Image and labeling workflows including Segmentation.

Labeling Experience

Machine Learning Engineer — European Molecular Biology Laboratory (EMBL)

OtherImageSegmentation

Developed, trained, and evaluated deep learning models for image segmentation and regression applied to spatial single-cell metabolomics and mass spectrometry data. Designed, tested, deployed, and maintained a Python-based GUI to support quality control and manual cell annotation workflows. Enabled fine-tuning of deep learning models through a user-friendly annotation interface. • Training/evaluation of segmentation and regression models for spatial single-cell data. • Building tooling for quality control of spatial single-cell metabolomics. • Supporting manual cell annotation and fine-tuning of deep learning models. • Maintaining and testing the annotation and analysis GUI software.

2021 - 2024

Machine Learning Engineer (Internship & PhD Research) — HCI–University of Heidelberg

OtherImageSegmentation

Developed and trained deep learning models for neuronal and image instance segmentation tasks, focusing on performance on large-scale datasets. Designed and implemented algorithms for image instance segmentation using Python and C++. Established analytical frameworks for agglomerative clustering on graphs to support image segmentation workflows. • Training and evaluating segmentation models for neuronal images. • Implementing instance segmentation algorithms in Python/C++. • Optimizing model performance on large datasets. • Supporting clustering-based segmentation using graph-based methods.

2016 - 2021

Education

Work History