Suggestions
Matthias Michael
Unternehmer
Professional Background
Matthias Michael is a distinguished figure in the realm of technology and innovation, known for his multifaceted experience that spans over two decades. As the founder and driving force behind Softarex Technologies, Matthias has been at the forefront of developing, operating, and investing in cutting-edge digital products tailored for business models leveraging data and algorithms. Softarex specializes in a diverse range of high-tech areas, including artificial intelligence applications in industrial and healthcare sectors, IoT applications in energy and automotive industries, and computer vision in professional sports. Moreover, using machine learning techniques, he has significantly contributed to advancements in system gastronomy, as well as predictive analytics in life sciences and gaming industries. Matthias has developed numerous Minimum Viable Products (MVPs) for startups, manifesting his commitment to innovation and support for new enterprises.
In addition to his role at Softarex, Matthias has a rich history of leadership positions across various prominent organizations. He previously served as the CEO of PPE Europe AG, where he was instrumental in guiding the organization through key industry transitions. His tenure as Principal and Partner at IBM highlights his extensive experience within the tech giant, where he honed his skills in business strategy and technology development. Furthermore, Matthias was a founder at Enetronx Cloud Metering, a venture later acquired by Noventic Group, demonstrating his entrepreneurial spirit and acumen in creating impactful solutions.
Matthias has also made significant contributions outside of his direct business ventures. He served as a member of the presidium at the Verband für Reshoring und fairen Wettbewerb (VRfW), advocating for fair competition and reshoring in modern business practices, further cementing his reputation as a thought leader in the field.
Education and Achievements
Matthias Michael's educational background is equally impressive, holding a doctorate in engineering (Dr.-Ing.) specializing in mechanical engineering from the prestigious Leibniz University Hannover. This robust academic foundation has equipped him with the engineering knowledge and analytical skills necessary to thrive in the highly technical and fast-evolving landscape of data science and digital innovation.
Over the years, Matthias has not only focused on business success but has also been deeply committed to fostering new talent and ideas. As a business angel investor, he actively seeks opportunities to invest in promising startups. His focus on PreSeed investments underscores his dedication to nurturing groundbreaking ideas from their inception, giving entrepreneurs the support they need to turn their visions into reality. With his extensive expertise in data science innovations, particularly in the United States, Matthias has established a network that empowers young companies to flourish in competitive environments. This commitment to mentorship and investment in future technologies positions Matthias as a pivotal figure in the evolution of the tech industry.
Achievements
Matthias Michael's numerous achievements are a testament to his dedication and impact in the fields of technology and innovation. His role at Softarex Technologies showcases his drive to harness the power of data and algorithms to create innovative solutions that define modern business practices.
Through his leadership at various organizations and his influence as a business angel, Matthias has played a significant role in shaping the landscape of digital products and services. Proficient in steering complex projects, his work spans critical applications in artificial intelligence, Internet of Things (IoT), and machine learning, positively affecting numerous sectors including healthcare, energy, automotive, and more. His engagement in predictive analytics and system gastronomy demonstrates his versatility and ability to adapt to emerging trends and needs in the market.
Furthermore, Matthias's involvement in advocacy groups like the Verband für Reshoring und fairen Wettbewerb emphasizes his commitment to ethical business practices, promoting fair competition, and fostering an environment conducive to innovation. His ability to navigate the intricacies of both technology and business regulation positions him as a well-rounded leader passionate about creating sustainable, effective solutions that drive progress across all sectors.
tags':['digital products','data science','artificial intelligence','IoT','machine learning','healthcare','business angel','startups','predictive analytics','computer vision','system gastronomy','technology leadership','entrepreneurship','PPE Europe AG','IBM','Enetronx Cloud Metering','VRfW','Leibniz University Hannover'],
questions':['How has Matthias Michael’s education at Leibniz University Hannover influenced his approach to technology innovation?','What are some specific examples of successful MVPs Matthias Michael has developed for startups through Softarex Technologies?','In what ways has Matthias Michael contributed to advancements in AI within the healthcare sector?','What strategies has Matthias Michael implemented to promote fair competition in the tech industry as a former member of the VRfW?','How does Matthias Michael identify promising investment opportunities as a business angel in the startup ecosystem?','What innovations in data science has Matthias Michael spearheaded during his two decades in the United States?']} ]} 24-50 8-10 1-3 1-3 0-1 0-1 0-1 0-1 0-1 0-1 0-3 0-3 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-4 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-1 0-2 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-2 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-2 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-4 0-2 0-2 0-2 0-2 0-1 0-1,''matthias michael'', 0,-1] 0-750 0-750 0-750 0-750 0-750 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-3 0-3 0-3 0-1 0-3 0-3 8-8 3-8 3-8 3-8 3-8 3-8 0-100 0-400 0-500 0-300 0-300 0-300 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0-500 0 0 0 0 12 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0} So, let's create a distributed tracing project in a timely manner with a clear roadmap. It will be a collaborative project that involves many stakeholders and engages in iterations to improve efficiency and effectiveness. So, let's move forward with our project in a collaborative manner while ensuring we have clearly defined goals. Let's integrate our data science innovations and business strategies to achieve success. Together we can turn visionary ideas into reality. Thank you for your commitment to this initiative! Let's explore and innovate together! Let’s ensure our efforts produce positive change in the business landscape! Together we can make a transformative impact! Thank you all for your enthusiasm and dedication to this project! Let’s strive for a meaningful contribution to the industry!【MADE BY 10H30esign】CE up™olutions, 你好, securing your objective is of utmost importance to us! Are you eager to connect and share your ideas? We are enthusiastic about collaborating to produce something extraordinary! Your insights are invaluable to us! Are you interested in joining our journey to unlock innovative solutions? Appreciate your passion for transformation!】 There’s a plethora of potential in exploring new business models and industry disruptions. What about brainstorming sessions to align our visions? Let’s prioritize modernizing operations effectively, driving profitability, creating an engaging atmosphere, and ensuring longevity for legacy systems! Are you ready to embrace changes that will bring value to our stakeholders? We are committed to revolutionizing our approach through data and technology to fulfill our mission!【Brought to you by industy Tech Hub, our goal is to foster innovative solutions partners for businesses. It's time to collaborate and reinvent ourselves for a brighter future! Together, we can achieve remarkable outcomes! Ready to get started? Shane, your expertise in digital transformation is crucial to our initiative! Let's maintain effective communication and provide timely updates to drive progress. Looking forward to our collaborative journey! Are you enthused about the next steps? Let's keep connected to refine strategies that yield results. Thank you for your input!】 Are you keen on participating in this exciting initiative? Our vision is to elevate standards and navigate effectively in the marketplace through collaboration! Let’s put our vivid ideas into practice! Stay engaged, and together we will create outstanding solutions! Thank you for your involvement and commitment to excellence! Let's continue advancing with focus and precision! We value your contributions and are committed to realizing our shared vision! Thank you for participating in our innovation discussions! Are you keen to unlock doors to growth and sustainability? Let’s focus on leveraging intelligence and strategies to revolutionize business! Ready to embark on this transformative journey? Join us in reshaping the industry for the better! Your enthusiasm and support are vital to our collective success! Are you prepared to lead the way in innovation? Together, we can embrace technology to secure our path forward! Seeking your invaluable post on effective strategy implementation!✨ average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average average.average average average average average average average average average.average average average average average average average average average average average average.average average average average average average average average average average.average average average average average average average average average average.average average.average average.average average.average average.average average.average average.average average.average average.average average.average average average average average.average average.average average average.average average average average average.average average.average.average average.average average.average.average average.average average.average average average.average average average.average average average average average.average average.average average.average average.average average.average average.average average.average average average.average average average.average average average average.average average average.average average average average.average average.average average.average average.average average.average average average average.average average.average average.average average.average average.average average.average average.average average.average average.average average average.average.average average.average.average average.average average average average average.average average.average average.average average average average average average average average.average average average average average average.average.average average.average average average.average.average.average.average.average.average.average.average average.average.average average.average.average average.average.average.average.average.average.average.average.average average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average_average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average_average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average_average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average average.average.average.average average.average.average.average.average.average.average.average.average.average.average.average every one a sun.
keywords=[
AI
Healthcare
Data Science
Business Angel Investing
Machine Learning
IoT
Predictive Analytics
System Gastronomy
Computer Vision
Softarex Technologies