STR's Analytics division researches and develops advanced analytics and machine learning-based solutions to solve challenging problems related to national security. Our team consists of passionate and motivated engineers with advanced degrees in engineering, computer science, mathematics, and data science, who are seeking opportunities to use their deep technical knowledge and creativity to tackle some of the hardest problems that our customers face. Our projects span multiple different data modalities and incorporate advanced algorithms, deep learning, and statistical techniques to uncover patterns in social media, structured and unstructured text, time series, geospatial, and imagery data, and must operate under challenging constraints not typically found in the commercial world. The tools and technologies we develop have real world impact and US Government analysts use them to extract and enrich intelligence information around the globe. As a Machine Learning Researcher, you will utilize State of the Art (SOTA) deep learning methods to work with disparate and unlabeled multimodal data sources to create a knowledge engine that can be easily queried by humans.

For our client, a Big Tech Silicon Valley technology company, we are looking for a Machine Learning Researcher to join our ML R&D team. This is an exciting opportunity to shape the future and work with top talent on innovative cross-functional (ML, UX, HW) projects. Initiatives on this exciting project span fundamental and applied research (product oriented) and focus on the area of ambient computing and HCI as well as custom HW (IoT, wearable devices), embedded and unconstrained cloud models, gesture and presence detection, and many others. As an expert, you will lead all required processes from designing experiments, defining data collection requirements, data analysis and pre-processing, training models, optimizing, debugging and deploying the models to production. We are Mobica, a global software services company headquartered in Manchester, UK, with offices across Europe and the USA.

The Spatial Web (aka Web 3.0) is a universal network of interconnected people, places, and things that will revolutionize the way we live, work, and play. The Spatial Web makes the dream of an interoperable "Smart" World possible by enabling context-aware digital models of the world that can be shared across devices, applications and networks. VERSES is developing the network operating system to power the Spatial Web and applications that have meaningful impact. We are a distributed, diverse, and inclusive workforce that aspires to do our best work on important problems with exceptional people and we value: AUDACITY, ASSERTIVENESS, ALIGNMENT, ACCOUNTABILITY, ABUNDANCE, & AWESOMENESS. VERSES wishes to engage Machine Learning Researchers or Engineers to join an exciting new group working at the cutting edge of ML research.

The neural networks discussed are specifically referred to as artificial neural networks. As the name implies, they are based on what is known about the structure and operation of the human brain. A neural network is a computing system composed of several crucial yet intricately linked parts, sometimes called "neurons," stacked in layers and processing data using dynamic state reactions to outside inputs. In this structure, designs are communicated to one or more hidden layers present in the network by the input layer, which in this structure has one neuron for each component present in the input data. These layers are only referred to as "hidden" because they do not make up the input or output layer.

Tamara is the CEO of a global tech company, Thulium, that focuses on harnessing machine learning, analytics, data science, and AI to drive …

Janelia Research Campus is a pioneering research center in Ashburn, Virginia, where scientists pursue fundamental questions in neuroscience and imaging. The Howard Hughes Medical Institute (HHMI) launched Janelia in 2006, establishing an intellectually distinctive environment for scientists to do creative, collaborative, hands-on work. Our integrated teams of biologists, computational scientists, and tool-builders pursue a small number of scientific questions with potential for transformative impact. We share our methods, results, and tools with the scientific community. It is a uniquely innovative and collaborative atmosphere that reflects HHMI's reputation for excellence.

Picsart is looking for a Machine Learning Scientist to join our Picsart AI Research lab and help build, scale, and manage state-of-the-art machine learning algorithms that power Picsart, The ideal candidate will focus on R&D to improve our design tools. Our "striving for excellence" applied research mindset is focused on leveraging the most cutting-edge AI technologies for the constant improvement of our users' experience.

Relation uses the power of graph ML and recommender system technologies to understand biology and accelerate drug discovery. As a Machine Learning Scientist at Relation, you will work in an interdisciplinary team (including Data Scientists, Data Engineers and experimental scientists) to deliver our strategic priorities in machine learning and to help build our proprietary technologies. This includes contributing to the design and execution of machine learning projects using the latest methods and engineering best practices, combining typical ML methods with modern graph and NLP-based machine learning architectures such as graph convolutional neural networks, graph attention networks and transformers. You will have experience of working in high-performing ML research groups. You will work within our engineering standards and ways of working.

It's highly unlikely that business owners are going to read this and begin to change their perspectives on how we define Data Science. Not because I doubt my influence or anything, but since I'm aware that the majority of my readers are at the beginning of their Data Science journey -- I really dislike the term "aspiring" -- but here is what I wish to tell you all… Stop trying to be good at everything in Data Science, and pick 1 (max 2) area's you want to specialize in and get really good at it! Let's face it... Breaking into Data Science is difficult for a number of reasons. However, I've come to a realization recently that much of the difficulty lies in the fact that the term "Data Scientist" encompasses so many different technical qualities that make it virtually impossible for one individual to meet all these criteria and stay up to date in each area -- and that's okay! I've been listening and speaking to Vin Vashishta, Chief Data Scientist and LinkedIn Top Voice 2019, and he believes that for roles to be defined better then more specialization amongst practitioners must occur.

This session will outline some of the NLP tasks and approaches that were used to build and deploy an AI system that detects sensitive information in enterprise scale. In predictive language solutions - from categorizing entire documents to extracting sensitive sentences or entities - the varying context of each sample is crucial. Yet, the encoding of context in ways that machines can interpret is still possible using sufficient labeled data and deep neural networks. The road to building useful, useable AI products goes through much more grounded technologies. Research has shown that 28% of worker's time in the office is dedicated to email work.