With a robust academic background and extensive post-doctoral experience at the intersection of biology and technology, this candidate currently contributes to innovative projects at Tulane University, USA, utilizing neural networks to enhance the world's largest fish image database for AI applications. Previous tenure at EMBL-EBI in the UK included the development of Python applications that revolutionized biological signal interpretation through machine learning and computer vision for analyzing cardiac rhythms and caudal movements. Experience at the National Institute for Amazonian Research in Brazil established a foundational expertise in scientific research, focusing on advanced technologies for analyzing captive animal behavior. Proficient in technologies such as Python, OpenCV, Scikit-learn, and TensorFlow, combined with a strong command of algorithms and data structures, positions this candidate to adeptly tackle complex challenges in the fields of data science and biology.

This candidate possesses a robust academic foundation in Computer Science, complemented by a Master's degree and ongoing doctoral studies in the same field. With extensive experience in Data Science projects concentrated on Machine Learning applications, they have demonstrated proficiency in a wide array of tools including pandas, numpy, scipy, matplotlib, seaborn, sklearn, pytorch, and various AutoML frameworks such as H2O and TPOT. Current roles include leading AI initiatives focused on vulnerability analysis in information systems and conducting research on imbalanced regression problems. Previous engagements encompass collaborative research on time series and machine learning for renewable energy generation forecasting, as well as academic positions teaching critical computer science subjects, reflecting a strong blend of practical and theoretical expertise.

Possessing a Bachelor's and a Master's degree in Physics from a state university, this candidate demonstrates expertise in applying mathematical tools, logical reasoning, and scientific methods to practical problem-solving across various domains that utilize modeling and data analysis. The individual showcases substantial proficiency in programming as applied to fields such as Analytics, Machine Learning, and finite element simulations, as well as in control and automation systems. With robust experience in developing and implementing AI, Computer Vision, and advanced machine learning solutions, this candidate has led multidisciplinary teams and adopted MLOps practices in the sector. Furthermore, they are well-versed in documenting projects effectively and delivering impactful oral presentations in both Portuguese and English, making them particularly suited for interdisciplinary collaborations.

A highly skilled data scientist and computer engineer with a Bachelor’s degree in Computer Engineering from Universidade de Araraquara, a Master’s in Computer Science and Computational Mathematics from the Instituto de Ciências Matemáticas e de Computação at USP, and a recent MBA in Data Science from USP/Esalq. Currently a doctoral candidate, engaged in advanced research focusing on machine learning applications for data quality assessment. Proven ability to apply theoretical knowledge to practical challenges, illustrated by substantial experience at Ford, where innovative facial recognition systems were developed utilizing advanced programming skills in Python and machine learning frameworks. Demonstrates strong analytical capabilities, collaborative spirit, and effective communication skills while mentoring MBA students.

A highly skilled professional in Electronic Engineering with a strong focus on Computer Vision and Artificial Intelligence, possessing robust qualifications through a Bachelor's and ongoing Master's degree in Electrical Engineering. Demonstrated expertise in image processing, object detection, anomaly classification, and semantic segmentation utilizing advanced frameworks such as PyTorch, TensorFlow, and OpenCV. Current research as an AI & Computer Vision Researcher involves developing software to inspect visual defects in notebooks using sophisticated techniques aligned with Agile methodology. Proficient in handling various communication protocols and embedded systems, combined with practical experience in natural language processing and thermographic imaging solutions. A commitment to innovation is evidenced by participation in international educational programs and ongoing professional development in cutting-edge technologies.

DeepMind AlphaFold is an advanced AI system designed to predict protein structures with high accuracy, aiding in biological and medical research by providing insights into protein folding and function.

A DeepMind AlphaFold expert must possess skills in computational biology, bioinformatics, and structural biology. Proficiency in machine learning, particularly deep learning, and experience with protein modeling software are also essential. Knowledge of programming languages such as Python and familiarity with high-performance computing environments are crucial.

How to hire a DeepMind AlphaFold expert

DeepMind AlphaFold was developed by DeepMind in 2018 to address the long-standing challenge of protein structure prediction. It achieved significant breakthroughs, notably winning the CASP13 competition in 2018 and CASP14 in 2020, demonstrating its ability to predict protein structures with unprecedented accuracy.

About DeepMind AlphaFold

Strengths of DeepMind AlphaFold include its high accuracy in predicting protein structures and its potential to accelerate scientific research. Weaknesses involve the complexity of interpreting some predictions and the need for extensive computational resources. Competitors include Rosetta, developed by the Baker Lab, and other AI-based protein prediction tools like those from the Zhang Lab.

DeepMind AlphaFold

Top 5*

Generative AI Tools

About DeepMind AlphaFold

How to hire a DeepMind AlphaFold expert

The Best of the Best Optimized for Your Budget